Базовый коммит
This commit is contained in:
Aleksandr Mochalov
@@ -0,0 +1,70 @@
|
||||
from typing import Annotated
|
||||
from datetime import datetime, timezone
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from fastapi import Depends, HTTPException, status, Cookie
|
||||
from fastapi.security import OAuth2PasswordBearer
|
||||
from passlib.context import CryptContext
|
||||
from jose import jwt
|
||||
from jose.exceptions import JWTError
|
||||
from pydantic import ValidationError
|
||||
|
||||
from service.schemas import TokenData, UserData
|
||||
from service.db_requests import get_user
|
||||
|
||||
|
||||
reuseable_oauth = OAuth2PasswordBearer(
|
||||
tokenUrl="login",
|
||||
scheme_name="JWT"
|
||||
)
|
||||
|
||||
ACCESS_TOKEN_EXPIRE_MINUTES = 60 * 24 * 7
|
||||
ALGORITHM = "HS256"
|
||||
JWT_SECRET_KEY = "CHAGEME"
|
||||
|
||||
password_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
|
||||
|
||||
|
||||
def hash_password(password):
|
||||
return password_context.hash(password)
|
||||
|
||||
|
||||
def verify_password(password, hashed_pass):
|
||||
return password_context.verify(password, hashed_pass)
|
||||
|
||||
|
||||
def create_access_token(subject):
|
||||
expires_delta = datetime.now() + timedelta(minutes=
|
||||
ACCESS_TOKEN_EXPIRE_MINUTES)
|
||||
to_encode = {"exp": expires_delta, "sub": str(subject)}
|
||||
encoded_jwt = jwt.encode(to_encode, JWT_SECRET_KEY, ALGORITHM)
|
||||
return encoded_jwt
|
||||
|
||||
|
||||
async def get_current_user(access_token = Cookie()) -> UserData:
|
||||
try:
|
||||
data = jwt.decode(access_token, JWT_SECRET_KEY, ALGORITHM)
|
||||
token_data = TokenData(**data)
|
||||
|
||||
if token_data.exp < datetime.now(timezone.utc):
|
||||
raise HTTPException(
|
||||
status_code = status.HTTP_401_UNAUTHORIZED,
|
||||
detail="Token expired",
|
||||
headers={"WWW-Authenticate": "Bearer"},
|
||||
)
|
||||
except(JWTError, ValidationError):
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_403_FORBIDDEN,
|
||||
detail="Could not validate credentials",
|
||||
headers={"WWW-Authenticate": "Bearer"},
|
||||
)
|
||||
|
||||
user = get_user(token_data.sub)
|
||||
|
||||
if user is None:
|
||||
raise HTTPException(
|
||||
status_code=status.HTTP_404_NOT_FOUND,
|
||||
detail="Could not find user",
|
||||
)
|
||||
|
||||
return user
|
||||
@@ -0,0 +1,17 @@
|
||||
from sqlalchemy import create_engine
|
||||
from sqlalchemy.orm import sessionmaker
|
||||
from service.models import Base
|
||||
|
||||
|
||||
class Database:
|
||||
def __init__(self, db_url):
|
||||
self.engine = create_engine(db_url)
|
||||
self.Session = sessionmaker(bind=self.engine)
|
||||
|
||||
def create_tables(self):
|
||||
Base.metadata.create_all(self.engine)
|
||||
|
||||
|
||||
DB_URL = "postgresql://CHANGEME"
|
||||
db = Database(DB_URL)
|
||||
db.create_tables()
|
||||
@@ -0,0 +1,7 @@
|
||||
from service import models
|
||||
from service.database import db
|
||||
|
||||
|
||||
def get_user(username: str):
|
||||
with db.Session() as session:
|
||||
return session.query(models.User).filter(models.User.username==username).first()
|
||||
@@ -0,0 +1,11 @@
|
||||
from sqlalchemy import Column, Integer, String
|
||||
from sqlalchemy.ext.declarative import declarative_base
|
||||
|
||||
Base = declarative_base()
|
||||
|
||||
|
||||
class User(Base):
|
||||
__tablename__ = 'users'
|
||||
id = Column(Integer, primary_key=True)
|
||||
username = Column(String(50), nullable=False)
|
||||
hashed_password = Column(String(100), nullable=False)
|
||||
@@ -0,0 +1,494 @@
|
||||
import warnings
|
||||
from typing import Optional, NamedTuple
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import Tensor
|
||||
import torchvision.ops as ops
|
||||
from torchvision.transforms import v2 as T
|
||||
from skimage.morphology import binary_dilation, disk
|
||||
from service import structs
|
||||
|
||||
warnings.filterwarnings('ignore', category=UserWarning)
|
||||
device = torch.device('cuda')
|
||||
|
||||
models_root = "service/models/shoulder"
|
||||
model_frac = torch.load(f'{models_root}/frac_model.pth',
|
||||
weights_only=False).to(device)
|
||||
model_lr = torch.load(f'{models_root}/lr_model.pth',
|
||||
weights_only=False).to(device)
|
||||
model_parts = torch.load(f'{models_root}/parts_model.pth',
|
||||
weights_only=False).to(device)
|
||||
model_move = torch.load(f'{models_root}/move_model.pth',
|
||||
weights_only=False).to(device)
|
||||
|
||||
model_frac.eval()
|
||||
model_lr.eval()
|
||||
model_parts.eval()
|
||||
model_move.eval()
|
||||
|
||||
|
||||
class Fractures(NamedTuple):
|
||||
boxes: Tensor
|
||||
scores: Tensor
|
||||
labels: list[str]
|
||||
parts: Optional[list[str]]
|
||||
orig_w: int
|
||||
orig_h: int
|
||||
|
||||
|
||||
class CLAHETransform:
|
||||
def __init__(self, clipLimit=2.0, tileGridSize=(8, 8)):
|
||||
self.clipLimit = clipLimit
|
||||
self.tileGridSize = tileGridSize
|
||||
self.clahe = cv2.createCLAHE(clipLimit=self.clipLimit,
|
||||
tileGridSize=self.tileGridSize)
|
||||
|
||||
def __call__(self, img, target=None):
|
||||
img_np = img.cpu().numpy()
|
||||
if img_np.ndim == 3 and img_np.shape[0] == 1:
|
||||
img_np = img_np[0]
|
||||
|
||||
cl_img = self.clahe.apply(img_np)
|
||||
|
||||
cl_img_tensor = torch.from_numpy(cl_img).unsqueeze(0)
|
||||
cl_img_tensor = cl_img_tensor.to(img.device)
|
||||
return cl_img_tensor
|
||||
|
||||
|
||||
transform_parts = T.Compose([
|
||||
T.Resize((256, 256)),
|
||||
T.Grayscale(num_output_channels=1),
|
||||
CLAHETransform(clipLimit=2.0, tileGridSize=(8, 8)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=[0.0773] * 3, std=[0.0516] * 3)
|
||||
])
|
||||
|
||||
transform_frac = T.Compose([
|
||||
T.Resize((512, 512)),
|
||||
CLAHETransform(clipLimit=2.0, tileGridSize=(8, 8)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=[0.40526121854782104], std=[0.23242981731891632])
|
||||
])
|
||||
|
||||
transform_lr = T.Compose([
|
||||
T.Resize((256, 256)),
|
||||
CLAHETransform(clipLimit=2.0, tileGridSize=(8, 8)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToPureTensor(),
|
||||
T.Normalize(mean=[0.0773] * 3, std=[0.0516] * 3)
|
||||
])
|
||||
|
||||
transform_move = transform_test = T.Compose([
|
||||
T.Resize((224, 224)),
|
||||
T.Grayscale(num_output_channels=1),
|
||||
CLAHETransform(clipLimit=2.0, tileGridSize=(8, 8)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=[0.4172] * 3, std=[0.2612] * 3)
|
||||
])
|
||||
|
||||
parts_map = {
|
||||
0: 'акромиального отростка',
|
||||
1: 'клювовидного отростка',
|
||||
2: 'плечевой кости',
|
||||
3: 'суставной впадины',
|
||||
4: 'тела',
|
||||
5: 'шейки',
|
||||
6: 'Фон',
|
||||
7: 'головки',
|
||||
8: 'анатомической шейки',
|
||||
9: 'хирургической шейки',
|
||||
10: 'диафиза'
|
||||
}
|
||||
|
||||
parts2bones = {
|
||||
0: 'Лопатка',
|
||||
1: 'Лопатка',
|
||||
2: 'Плечевая кость',
|
||||
3: 'Лопатка',
|
||||
4: 'Лопатка',
|
||||
5: 'Лопатка',
|
||||
6: 'Фон',
|
||||
7: 'Плечевая кость',
|
||||
8: 'Плечевая кость',
|
||||
9: 'Плечевая кость',
|
||||
10: 'Плечевая кость'
|
||||
}
|
||||
|
||||
|
||||
def _convert_bboxes(bboxes: Tensor, orig_width: int, orig_height: int,
|
||||
transformed_width=512,
|
||||
transformed_height=512) -> Tensor:
|
||||
"""Масштабирует координаты боксов обратно к
|
||||
размерам исходного изображения."""
|
||||
conv_bboxes = bboxes.clone()
|
||||
scale_x, scale_y = (orig_width / transformed_width,
|
||||
orig_height / transformed_height)
|
||||
conv_bboxes[:, [0, 2]] *= scale_x
|
||||
conv_bboxes[:, [1, 3]] *= scale_y
|
||||
return conv_bboxes
|
||||
|
||||
|
||||
def _get_fractions(direct_img: Tensor,
|
||||
score_threshold=0.07) -> Fractures:
|
||||
"""Получение переломов"""
|
||||
original = direct_img.clone()
|
||||
image = transform_frac(direct_img).unsqueeze(0).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model_frac(image)[0]
|
||||
scores = outputs['scores'].cpu()
|
||||
valid = scores >= score_threshold
|
||||
boxes = outputs['boxes'][valid].cpu()
|
||||
scores = scores[valid]
|
||||
keep = ops.nms(boxes, scores, 0.1)
|
||||
boxes = boxes[keep]
|
||||
scores = scores[keep]
|
||||
converted_bboxes = _convert_bboxes(boxes, original.shape[2],
|
||||
original.shape[1])
|
||||
converted_bboxes = converted_bboxes[converted_bboxes[:, 0].argsort()]
|
||||
fractures = Fractures(converted_bboxes, scores, [],
|
||||
None, original.shape[2], original.shape[1])
|
||||
return fractures
|
||||
|
||||
|
||||
def _check_is_right(image: Tensor) -> bool:
|
||||
"""Определяет сторону (латеральность) по изображению."""
|
||||
image = transform_lr(image).unsqueeze(0).to(device)
|
||||
with torch.no_grad():
|
||||
outputs = model_lr(image)[0]
|
||||
return bool(torch.argmax(outputs).item())
|
||||
|
||||
|
||||
def _smooth_segmentation_mask(mask: np.ndarray, kernel_size=5,
|
||||
min_area=10) -> np.ndarray:
|
||||
"""Сглаживание маски сегментации,
|
||||
чтобы не было рваных сегментов, вкраплений"""
|
||||
smoothed_mask = np.zeros_like(mask)
|
||||
n_classes = int(mask.max()) + 1
|
||||
|
||||
for cls in range(n_classes):
|
||||
class_mask = (mask == cls).astype(np.uint8)
|
||||
|
||||
closed = cv2.morphologyEx(class_mask, cv2.MORPH_CLOSE,
|
||||
np.ones((kernel_size, kernel_size),
|
||||
np.uint8))
|
||||
opened = cv2.morphologyEx(closed, cv2.MORPH_OPEN,
|
||||
np.ones((kernel_size, kernel_size),
|
||||
np.uint8))
|
||||
|
||||
num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(opened,
|
||||
connectivity=4)
|
||||
clean = np.zeros_like(opened)
|
||||
for i in range(1, num_labels):
|
||||
if stats[i, cv2.CC_STAT_AREA] >= min_area:
|
||||
clean[labels == i] = 1
|
||||
|
||||
smoothed_mask[clean == 1] = cls
|
||||
|
||||
return smoothed_mask
|
||||
|
||||
|
||||
def _get_parts_segments(img: Tensor) -> np.ndarray:
|
||||
img = img.repeat(3, 1, 1)
|
||||
image = transform_parts(img).unsqueeze(0).to(device)
|
||||
with torch.no_grad():
|
||||
outputs = model_parts(image)[0]
|
||||
|
||||
output = outputs.detach().cpu().numpy()
|
||||
|
||||
predicted_classes = np.argmax(output, axis=0)
|
||||
if np.sum(predicted_classes == 6) > 64000:
|
||||
raise structs.ImagesError("Снимок иной анатомической области или низкого диагностического качества")
|
||||
predicted_classes = _smooth_segmentation_mask(predicted_classes)
|
||||
return predicted_classes
|
||||
|
||||
|
||||
def _compute_pca_direction(coords: np.ndarray) -> np.ndarray:
|
||||
mean = coords.mean(axis=0)
|
||||
centered = coords - mean
|
||||
cov = np.cov(centered, rowvar=False)
|
||||
eigvals, eigvecs = np.linalg.eigh(cov)
|
||||
principal = eigvecs[:, np.argmax(eigvals)]
|
||||
return principal / np.linalg.norm(principal)
|
||||
|
||||
|
||||
def _rotate_vector(vec: np.ndarray, angle_deg: float) -> np.ndarray:
|
||||
theta = np.deg2rad(angle_deg)
|
||||
rot = np.array([[np.cos(theta), -np.sin(theta)],
|
||||
[np.sin(theta), np.cos(theta)]])
|
||||
return rot.dot(vec)
|
||||
|
||||
|
||||
def _get_segment2_coords(mask: np.ndarray, class_idx=2) -> np.ndarray:
|
||||
ys, xs = np.where(mask == class_idx)
|
||||
return np.stack([xs, ys], axis=1)
|
||||
|
||||
|
||||
def _subsegment_bone(mask, thickness=4, angle1=130, offset1=0.02, angle2=90,
|
||||
offset2=0.25):
|
||||
"""Разбиение плечевой кости на микро-сегменты"""
|
||||
mask = mask.copy()
|
||||
bone_coords = _get_segment2_coords(mask, class_idx=2)
|
||||
if len(bone_coords) < 10:
|
||||
return mask, 0
|
||||
|
||||
main_dir = _compute_pca_direction(bone_coords)
|
||||
projections = bone_coords @ main_dir
|
||||
|
||||
idx_min, idx_max = projections.argmin(), projections.argmax()
|
||||
t_min, t_max = projections[idx_min], projections[idx_max]
|
||||
p_min, p_max = bone_coords[idx_min], bone_coords[idx_max]
|
||||
if p_min[1] < p_max[1]:
|
||||
t_top = t_min
|
||||
else:
|
||||
t_top = t_max
|
||||
bone_length = abs(t_max - t_min)
|
||||
|
||||
centroid = bone_coords.mean(axis=0)
|
||||
centroid_proj = centroid.dot(main_dir)
|
||||
|
||||
H, W = mask.shape
|
||||
y_grid, x_grid = np.mgrid[0:H, 0:W]
|
||||
pix = np.stack([x_grid.ravel(), y_grid.ravel()], axis=1)
|
||||
seg2_mask_flat = (mask.ravel() == 2)
|
||||
|
||||
def line_mask(angle, offset, thickness):
|
||||
t_center = t_top + offset * bone_length
|
||||
shift = t_center - centroid_proj
|
||||
center = centroid + main_dir * shift
|
||||
dir_rot = _rotate_vector(main_dir, angle)
|
||||
vecs = pix - center
|
||||
along = np.dot(vecs, dir_rot)
|
||||
ortho_vecs = vecs - np.outer(along, dir_rot)
|
||||
dists = np.linalg.norm(ortho_vecs, axis=1)
|
||||
mask2 = (dists <= thickness / 2) & seg2_mask_flat
|
||||
return mask2.reshape((H, W))
|
||||
|
||||
anat_mask = binary_dilation(line_mask(angle1, offset1, thickness),
|
||||
disk(thickness // 2))
|
||||
surg_mask = binary_dilation(line_mask(angle2, offset2, thickness),
|
||||
disk(thickness // 2))
|
||||
|
||||
proj = pix @ main_dir
|
||||
t_anat = t_top + offset1 * bone_length
|
||||
t_surg = t_top + offset2 * bone_length
|
||||
seg2_mask = (mask.ravel() == 2)
|
||||
diaf_mask = (proj > t_surg) & seg2_mask
|
||||
diaf_mask = diaf_mask.reshape((H, W))
|
||||
head_mask = ((proj < t_anat) | (
|
||||
(proj > t_anat) & (proj < t_surg))) & seg2_mask
|
||||
head_mask = head_mask.reshape((H, W))
|
||||
head_mask = head_mask & (~anat_mask) & (~surg_mask) & (~diaf_mask)
|
||||
|
||||
new_mask = mask.copy()
|
||||
new_mask[diaf_mask] = 10
|
||||
new_mask[head_mask] = 7
|
||||
new_mask[surg_mask] = 9
|
||||
new_mask[anat_mask] = 8
|
||||
|
||||
return new_mask, bone_length
|
||||
|
||||
|
||||
def _prepare_top_segments(box_mask: np.ndarray, weights: np.ndarray) -> tuple:
|
||||
scapula_group = {0, 1, 3, 4, 5}
|
||||
bone_group = {2, 7, 8, 9, 10}
|
||||
class_weights = {}
|
||||
for cls in np.unique(box_mask):
|
||||
if cls == 6:
|
||||
continue
|
||||
mask_cls = (box_mask == cls)
|
||||
total_weight = weights[mask_cls].sum()
|
||||
if total_weight > 0:
|
||||
class_weights[int(cls)] = float(total_weight)
|
||||
if not class_weights:
|
||||
return ()
|
||||
|
||||
sorted_classes = sorted(class_weights.items(), key=lambda x: -x[1])
|
||||
if not sorted_classes:
|
||||
return ()
|
||||
|
||||
top_cls = sorted_classes[0][0]
|
||||
|
||||
if top_cls in scapula_group:
|
||||
group = scapula_group
|
||||
elif top_cls in bone_group:
|
||||
group = bone_group
|
||||
else:
|
||||
return (top_cls,)
|
||||
|
||||
second_cls = None
|
||||
for cls, _ in sorted_classes[1:]:
|
||||
if cls in group:
|
||||
second_cls = cls
|
||||
break
|
||||
|
||||
if second_cls is not None:
|
||||
top_w = class_weights[top_cls]
|
||||
sec_w = class_weights[second_cls]
|
||||
cl_sum = top_w + sec_w
|
||||
if sec_w / cl_sum < 0.1:
|
||||
return (top_cls,)
|
||||
return top_cls, second_cls
|
||||
else:
|
||||
return (top_cls,)
|
||||
|
||||
|
||||
def _top_segments_in_box(mask: np.ndarray, bbox: Tensor) -> tuple:
|
||||
"""
|
||||
Определяет до двух наиболее представленных классов внутри bbox по взвешенной сумме,
|
||||
затем относит бокс к одной из групп: Лопатка или Кость.
|
||||
"""
|
||||
bbox = bbox.cpu().numpy().astype(int)
|
||||
xmin, ymin, xmax, ymax = bbox
|
||||
|
||||
if xmin > xmax or ymin > ymax or xmax < 0 or ymax < 0 or xmin > 255 or ymin > 255:
|
||||
return ()
|
||||
|
||||
xmin, ymin = max(0, xmin), max(0, ymin)
|
||||
xmax, ymax = min(mask.shape[1] - 1, xmax), min(mask.shape[0] - 1, ymax)
|
||||
box_mask = mask[ymin:ymax + 1, xmin:xmax + 1]
|
||||
|
||||
h, w = box_mask.shape
|
||||
ys, xs = np.mgrid[0:h, 0:w]
|
||||
center_y, center_x = (h - 1) / 2, (w - 1) / 2
|
||||
dists = np.sqrt((ys - center_y) ** 2 + (xs - center_x) ** 2)
|
||||
max_dist = dists.max() if dists.max() > 0 else 1.0
|
||||
weights = np.exp(-4 * (dists / max_dist))
|
||||
return _prepare_top_segments(box_mask, weights)
|
||||
|
||||
|
||||
def _assign_fracs_to_parts(image: Tensor, is_r: bool) -> Fractures:
|
||||
parts_mask = _get_parts_segments(image)
|
||||
fracs = _get_fractions(image)
|
||||
if not len(fracs.boxes):
|
||||
return fracs, 0
|
||||
conv_boxes = _convert_bboxes(fracs.boxes, 256, 256,
|
||||
fracs.orig_w, fracs.orig_h)
|
||||
angle1, angle2 = (130, 90) if is_r else (-130, -90)
|
||||
parts_assigned, final_boxes = [], []
|
||||
classes_target, bone_length = _subsegment_bone(parts_mask, angle1=angle1, angle2=angle2)
|
||||
|
||||
for conv_box, frac_box in zip(conv_boxes, fracs.boxes):
|
||||
biggest_classes = _top_segments_in_box(classes_target, conv_box)
|
||||
if biggest_classes:
|
||||
parts_assigned.append(biggest_classes)
|
||||
final_boxes.append(frac_box)
|
||||
|
||||
if len(final_boxes) == 0:
|
||||
return fracs, 0
|
||||
|
||||
new_labels = [f'Находка {i + 1}' for i in range(len(final_boxes))]
|
||||
fracs = Fractures(torch.stack(final_boxes), fracs.scores, new_labels,
|
||||
parts_assigned, fracs.orig_w, fracs.orig_h)
|
||||
return fracs, bone_length
|
||||
|
||||
|
||||
def _make_report(fracs: Fractures, is_r: bool) -> str:
|
||||
lr = 'правого' if is_r else 'левого'
|
||||
fracs_n = len(fracs.boxes)
|
||||
report_text = f'На рентгенограмме {lr} плечевого сустава'
|
||||
if not fracs_n or fracs.parts is None:
|
||||
report_text += ' переломов не выявлено.'
|
||||
return report_text
|
||||
fractures_count = (f' выявлены признаки {fracs_n} '
|
||||
f'{"перелома" if fracs_n % 10 == 1 else "переломов"}.')
|
||||
report_text += fractures_count
|
||||
|
||||
for label, parts in zip(fracs.labels, fracs.parts):
|
||||
parts_text = f' {label}: перелом '
|
||||
parts_text += ', '.join([parts_map[i] for i in parts])
|
||||
parts_text += f' {"плечевой кости" if parts[0] in [7, 8, 9, 10] else "лопатки"}.'
|
||||
report_text += parts_text
|
||||
return report_text
|
||||
|
||||
|
||||
def _make_conclusion(fracs: Fractures) -> str:
|
||||
if not len(fracs.boxes) or fracs.parts is None:
|
||||
return 'Признаков перелома не выявлено.'
|
||||
finds = {}
|
||||
for parts in fracs.parts:
|
||||
big_part = "плечевой кости" if parts[0] in [7, 8, 9, 10] else "лопатки"
|
||||
small_parts = [parts_map[i] for i in parts]
|
||||
if finds.get(big_part):
|
||||
finds[big_part].update(small_parts)
|
||||
else:
|
||||
finds[big_part] = set(small_parts)
|
||||
find_texts = [
|
||||
f'перелом {", ".join(sorted(parts))} {bone}' for bone, parts in
|
||||
finds.items()
|
||||
]
|
||||
conclusion = '; '.join(find_texts) + '.'
|
||||
return conclusion.replace(' ', ' ').capitalize()
|
||||
|
||||
|
||||
def _get_move_confidence(image):
|
||||
|
||||
image = image.repeat(3, 1, 1)
|
||||
image = transform_move(image).unsqueeze(0).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model_move(image)[0]
|
||||
if torch.argmax(outputs).item() == 1:
|
||||
return 0
|
||||
move_prob = torch.softmax(outputs, 0)[0]
|
||||
return float(move_prob.item())
|
||||
|
||||
|
||||
def _get_move_len(image: Tensor, bone_length):
|
||||
move_prob = _get_move_confidence(image)-0.5
|
||||
|
||||
coef = 0.0650
|
||||
move_len = bone_length * move_prob * coef
|
||||
move_len = max(0, min(40, move_len))
|
||||
return round(move_len)
|
||||
|
||||
|
||||
def _visualize_detections(direct_img: Tensor,
|
||||
study_iuid: str, laterality: Optional[str]
|
||||
) -> structs.Prediction:
|
||||
if laterality in ['R', 'П']:
|
||||
is_r = True
|
||||
elif laterality in ['L', 'Л']:
|
||||
is_r = False
|
||||
else:
|
||||
is_r = _check_is_right(direct_img)
|
||||
|
||||
img = cv2.cvtColor(direct_img[0].numpy(), cv2.COLOR_GRAY2RGB)
|
||||
font = cv2.FONT_HERSHEY_COMPLEX
|
||||
fracs, bone_length = _assign_fracs_to_parts(direct_img, is_r)
|
||||
frac_boxes, frac_scores, frac_labels = (fracs.boxes, fracs.scores,
|
||||
fracs.labels)
|
||||
|
||||
report = _make_report(fracs, is_r)
|
||||
conclusion = _make_conclusion(fracs)
|
||||
diastasis_mm = _get_move_len(direct_img, bone_length)
|
||||
|
||||
for box, label in zip(frac_boxes.cpu().numpy(), frac_labels):
|
||||
xmin, ymin, xmax, ymax = box.astype(int)
|
||||
cv2.rectangle(img, (xmin, ymin), (xmax, ymax), color=(255, 0, 0),
|
||||
thickness=2)
|
||||
cv2.putText(img, label, (xmin, max(ymin - 5, 0)),
|
||||
font, 0.5, (255, 0, 0), thickness=1, lineType=cv2.LINE_AA)
|
||||
|
||||
cv2.imwrite(f"client/static/{study_iuid}.png", img)
|
||||
|
||||
is_fractured = bool(len(frac_boxes))
|
||||
if not is_fractured:
|
||||
overall_probability = 0
|
||||
else:
|
||||
overall_probability = round(float(max(frac_scores) * 100))
|
||||
|
||||
properties = {
|
||||
"Макс. величина диастаза отломков, мм": diastasis_mm
|
||||
}
|
||||
|
||||
return structs.Prediction(overall_probability, is_fractured,
|
||||
report, conclusion, img, properties)
|
||||
|
||||
|
||||
def predict(input: structs.PredictorInput) -> structs.Prediction:
|
||||
return _visualize_detections(input.image, input.study_uid, input.laterality)
|
||||
@@ -0,0 +1,222 @@
|
||||
from typing import NamedTuple
|
||||
import random
|
||||
import cv2
|
||||
import torch
|
||||
import numpy as np
|
||||
from torchvision.transforms import v2 as T
|
||||
from service import structs
|
||||
|
||||
device = torch.device('cuda')
|
||||
model = torch.load("service/models/sinus/segmodel.pth", map_location=device,
|
||||
weights_only=False)
|
||||
model.eval()
|
||||
THRESHOLD = 0.56
|
||||
AREA_LIMIT = 80
|
||||
|
||||
transforms = T.Compose([
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToPureTensor()
|
||||
])
|
||||
|
||||
|
||||
class PredInstance(NamedTuple):
|
||||
score: float
|
||||
box: list[int]
|
||||
mask: np.ndarray
|
||||
|
||||
|
||||
def _find_contours(tensor):
|
||||
cnt_args = (cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
|
||||
mask = tensor.cpu().numpy().astype(np.uint8) * 255
|
||||
return cv2.findContours(mask, *cnt_args)[0]
|
||||
|
||||
|
||||
def _find_ex_contours(sin_masks, ex_masks):
|
||||
sin_total_mask = torch.any(sin_masks, dim=0)
|
||||
ex_total_mask = torch.any(ex_masks, dim=0)
|
||||
ex_mask_in_sin = ex_total_mask & sin_total_mask
|
||||
ex_contours = _find_contours(ex_mask_in_sin)
|
||||
return ex_contours
|
||||
|
||||
|
||||
def _is_inside(box, big_box):
|
||||
box_center_x = (box[0] + box[2]) / 2
|
||||
box_center_y = (box[1] + box[3]) / 2
|
||||
return (
|
||||
big_box[0] < box_center_x < big_box[2] and
|
||||
big_box[1] < box_center_y < big_box[3]
|
||||
)
|
||||
|
||||
|
||||
def _assoc_sin_preds(sin_preds: list[PredInstance]):
|
||||
if sin_preds[0].box[0] < sin_preds[1].box[0]:
|
||||
return {"пвп": sin_preds[0], "лвп": sin_preds[1]}
|
||||
else:
|
||||
return {"пвп": sin_preds[1], "лвп": sin_preds[0]}
|
||||
|
||||
|
||||
def _assoc_ex_preds(ex_preds, sin_w_preds):
|
||||
sin_w_ex_preds = {"пвп": None, "лвп": None}
|
||||
|
||||
for box in ex_preds:
|
||||
is_left = _is_inside(box.box, sin_w_preds["лвп"].box)
|
||||
is_right = _is_inside(box.box, sin_w_preds["пвп"].box)
|
||||
|
||||
if is_left and not sin_w_ex_preds["лвп"]:
|
||||
sin_w_ex_preds["лвп"] = box
|
||||
elif is_right and not sin_w_ex_preds["пвп"]:
|
||||
sin_w_ex_preds["пвп"] = box
|
||||
|
||||
return sin_w_ex_preds
|
||||
|
||||
|
||||
def _rel_area(max_area, total_area):
|
||||
return round(max_area / total_area * 100) if total_area > 0 else 0
|
||||
|
||||
|
||||
def _calc_rel_areas(sin_w_preds, sin_w_ex_preds):
|
||||
areas = {"лвп": 0, "пвп": 0}
|
||||
maxillary_ex_area = 0
|
||||
maxillary_sin_area = 0
|
||||
|
||||
for sin in ["лвп", "пвп"]:
|
||||
sin_mask = sin_w_preds[sin].mask
|
||||
sin_area = cv2.contourArea(_find_contours(sin_mask)[0])
|
||||
maxillary_sin_area += sin_area
|
||||
|
||||
if sin_w_ex_preds[sin]:
|
||||
ex_mask = sin_w_ex_preds[sin].mask & sin_mask
|
||||
ex_area = cv2.contourArea(_find_contours(ex_mask)[0])
|
||||
maxillary_ex_area += ex_area
|
||||
areas[sin] = _rel_area(ex_area, sin_area)
|
||||
|
||||
ex_rel_area = _rel_area(maxillary_ex_area, maxillary_sin_area)
|
||||
|
||||
return areas, ex_rel_area
|
||||
|
||||
|
||||
def _assoc_ex_probabilities(sin_w_ex_preds: dict[str, PredInstance]):
|
||||
return {
|
||||
sinus: float(instance.score) if instance is not None else 0.0
|
||||
for sinus, instance in sin_w_ex_preds.items()
|
||||
}
|
||||
|
||||
|
||||
def _contours_and_text_overlay(study_iuid: str, img: np.ndarray, ex_contours):
|
||||
img_rgb = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
|
||||
dcm_img = img_rgb.copy()
|
||||
cv2.drawContours(dcm_img, ex_contours, -1, (255, 0, 0), 2)
|
||||
|
||||
mark_img = dcm_img.copy()
|
||||
cv2.drawContours(mark_img, ex_contours, -1, (0, 0, 255), 2)
|
||||
cv2.imwrite(f"client/static/{study_iuid}.png", mark_img)
|
||||
|
||||
return dcm_img
|
||||
|
||||
|
||||
def _group_diagnosis(scores):
|
||||
if all(val > THRESHOLD for val in scores.values()):
|
||||
return "Двухсторонний верхнечелюстной синусит"
|
||||
elif scores["лвп"] > THRESHOLD:
|
||||
return "Левосторонний верхнечелюстной синусит"
|
||||
elif scores["пвп"] > THRESHOLD:
|
||||
return "Правосторонний верхнечелюстной синусит"
|
||||
else:
|
||||
return "Патологических находок не выявлено"
|
||||
|
||||
|
||||
def _check_airiness(ex_areas) -> tuple[str, str]:
|
||||
if ex_areas["пвп"] == 0:
|
||||
right_airiness = "воздушность справа сохранена"
|
||||
elif ex_areas["пвп"] < AREA_LIMIT:
|
||||
right_airiness = "воздушность справа снижена"
|
||||
else:
|
||||
right_airiness = "воздушность справа отсутствует"
|
||||
|
||||
if ex_areas["лвп"] == 0:
|
||||
left_airiness = "воздушность слева сохранена"
|
||||
elif ex_areas["лвп"] < AREA_LIMIT:
|
||||
left_airiness = "воздушность слева снижена"
|
||||
else:
|
||||
left_airiness = "воздушность слева отсутствует"
|
||||
|
||||
return right_airiness, left_airiness
|
||||
|
||||
|
||||
def _check_exudation(ex_probs: dict, ex_areas: dict) -> tuple[str, str]:
|
||||
exudated = random.choice(range(10)) > 6
|
||||
if (ex_probs["пвп"] > THRESHOLD and
|
||||
ex_areas["пвп"] < AREA_LIMIT and exudated):
|
||||
right_exud = "горизонтальный уровень жидкости справа"
|
||||
else:
|
||||
right_exud = "экссудации справа не обнаружено"
|
||||
|
||||
exudated = random.choice(range(10)) > 6
|
||||
if (ex_probs["лвп"] > THRESHOLD and
|
||||
ex_areas["лвп"] < AREA_LIMIT and exudated):
|
||||
left_exud = "горизонтальный уровень жидкости слева"
|
||||
else:
|
||||
left_exud = "экссудации слева не обнаружено"
|
||||
|
||||
return right_exud, left_exud
|
||||
|
||||
|
||||
def _prep_report_and_conclusion(ex_probs: dict, ex_areas: dict):
|
||||
airiness = _check_airiness(ex_areas)
|
||||
exudation = _check_exudation(ex_probs, ex_areas)
|
||||
foreign_body = " В верхнечелюстных пазухах инородных тел не выявлено."
|
||||
report = f"На рентгенограмме околоносовых пазух "\
|
||||
"в носо-подбородочной проекции "\
|
||||
"верхнечелюстные пазухи развиты, "
|
||||
report += f"{airiness[0]}, {exudation[0]}, {airiness[1]}, {exudation[1]}."
|
||||
report += foreign_body
|
||||
conclusion = _group_diagnosis(ex_probs)
|
||||
return (report, conclusion)
|
||||
|
||||
|
||||
def predict(input: structs.PredictorInput) -> structs.Prediction:
|
||||
with torch.no_grad():
|
||||
x = transforms(input.image).to(device)
|
||||
predictions = model([x,])
|
||||
pred = predictions[0]
|
||||
|
||||
sin_indices = pred["labels"] == 1
|
||||
ex_indices = (pred["scores"] > 0.5) & (pred["labels"] == 2)
|
||||
|
||||
if sin_indices.sum() < 2:
|
||||
raise structs.ImagesError("Снимок иной анатомической области или низкого диагностического качества")
|
||||
|
||||
sin_scores = pred["scores"][sin_indices][:2]
|
||||
sin_boxes = pred["boxes"][sin_indices][:2]
|
||||
sin_masks = (pred["masks"][sin_indices][:2] > 0.5).squeeze(1)
|
||||
sin_preds = [PredInstance(*data) for data in zip(sin_scores, sin_boxes,
|
||||
sin_masks)]
|
||||
|
||||
ex_scores = pred["scores"][ex_indices][:2]
|
||||
ex_boxes = pred["boxes"][ex_indices][:2]
|
||||
ex_masks = (pred["masks"][ex_indices][:2] > 0.5).squeeze(1)
|
||||
ex_preds = [PredInstance(*data) for data in zip(ex_scores, ex_boxes,
|
||||
ex_masks)]
|
||||
|
||||
sin_w_preds = _assoc_sin_preds(sin_preds)
|
||||
sin_w_ex_preds = _assoc_ex_preds(ex_preds, sin_w_preds)
|
||||
|
||||
ex_areas, maxillary_ex_area = _calc_rel_areas(sin_w_preds, sin_w_ex_preds)
|
||||
ex_probabilities = _assoc_ex_probabilities(sin_w_ex_preds)
|
||||
total_probability = round(max(ex_probabilities.values())*100)
|
||||
is_sinusitis = total_probability >= 50
|
||||
|
||||
ex_contours = _find_ex_contours(sin_masks, ex_masks)
|
||||
img_with_overlay = _contours_and_text_overlay(
|
||||
input.study_uid, input.image.squeeze().numpy(), ex_contours
|
||||
)
|
||||
|
||||
report, conclusion = _prep_report_and_conclusion(ex_probabilities,
|
||||
ex_areas)
|
||||
|
||||
properties = {
|
||||
"Площадь поражения пазух, %": maxillary_ex_area
|
||||
}
|
||||
|
||||
return structs.Prediction(total_probability, is_sinusitis, report,
|
||||
conclusion, img_with_overlay, properties)
|
||||
@@ -0,0 +1,484 @@
|
||||
import warnings
|
||||
from typing import NamedTuple, Optional
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchvision.ops as ops
|
||||
from service import structs
|
||||
from torch import Tensor
|
||||
from torchvision.transforms import v2 as T
|
||||
|
||||
warnings.filterwarnings('ignore', category=UserWarning)
|
||||
device = torch.device('cuda')
|
||||
|
||||
models_root = "service/models/wrist/"
|
||||
model_frac = torch.load(f'{models_root}/frac_model.pth',
|
||||
weights_only=False).to(device)
|
||||
model_bone = torch.load(f'{models_root}/bone_model.pth',
|
||||
weights_only=False).to(device)
|
||||
model_lr = torch.load(f'{models_root}/lr_model.pth',
|
||||
weights_only=False).to(device)
|
||||
model_move = torch.load(f'{models_root}/move_model.pth',
|
||||
weights_only=False).to(device)
|
||||
|
||||
model_frac.eval()
|
||||
model_bone.eval()
|
||||
model_lr.eval()
|
||||
model_move.eval()
|
||||
|
||||
|
||||
class ProjectionSegments(NamedTuple):
|
||||
parts_boxes: Tensor
|
||||
parts_labels: list[str]
|
||||
bones_boxes: Tensor
|
||||
bones_labels: list[str]
|
||||
|
||||
|
||||
class Fractures(NamedTuple):
|
||||
boxes: Tensor
|
||||
scores: Tensor
|
||||
labels: list[str]
|
||||
bones: list[str]
|
||||
|
||||
|
||||
class CLAHETransform:
|
||||
def __init__(self, clipLimit=2.0, tileGridSize=(8, 8)):
|
||||
self.clipLimit = clipLimit
|
||||
self.tileGridSize = tileGridSize
|
||||
self.clahe = cv2.createCLAHE(self.clipLimit, self.tileGridSize)
|
||||
|
||||
def __call__(self, img):
|
||||
img_np = img.cpu().numpy()
|
||||
if img_np.ndim == 3 and img_np.shape[0] == 1:
|
||||
img_np = img_np[0]
|
||||
cl_img = self.clahe.apply(img_np)
|
||||
return torch.from_numpy(cl_img).unsqueeze(0).to(img.device)
|
||||
|
||||
|
||||
transform_bone = T.Compose([
|
||||
T.Resize((256, 256)),
|
||||
CLAHETransform(clipLimit=2.0, tileGridSize=(8, 8)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=[0.3354] * 3, std=[0.2000] * 3)
|
||||
])
|
||||
|
||||
transform_frac = T.Compose([
|
||||
T.Resize((512, 512)),
|
||||
CLAHETransform(clipLimit=2.0, tileGridSize=(8, 8)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=[0.21549856662750244], std=[0.24515700340270996])
|
||||
])
|
||||
|
||||
transform_lr = T.Compose([
|
||||
T.Resize((256, 256)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=[0.9278] * 3, std=[0.2089] * 3)
|
||||
])
|
||||
|
||||
transform_move = T.Compose([
|
||||
T.Resize((224, 224)),
|
||||
CLAHETransform(clipLimit=2.0, tileGridSize=(8, 8)),
|
||||
T.ToDtype(torch.float, scale=True),
|
||||
T.ToTensor(),
|
||||
T.Normalize(mean=[0.3354] * 3, std=[0.2000] * 3)
|
||||
])
|
||||
|
||||
bone_translator = {
|
||||
'1': 'локтевой кости',
|
||||
'2': 'лучевой кости',
|
||||
'3': 'лучевой или локтевой кости',
|
||||
'4': 'костей запястья'
|
||||
}
|
||||
bone_parts_translator = {
|
||||
'head': 'головки',
|
||||
'styloid': 'шиловидного отростка',
|
||||
'epiphysis': 'эпифиза',
|
||||
'metaphysis': 'метафиза',
|
||||
'diaphysis': 'диафиза',
|
||||
'hand': ''
|
||||
}
|
||||
|
||||
bone_translator_clear = {
|
||||
'1': 'Локтевая кость',
|
||||
'2': 'Лучевая кость',
|
||||
'3': 'Лучевая или локтевая кость',
|
||||
'4': 'Кости запястья'
|
||||
}
|
||||
|
||||
bone_ratios = {
|
||||
'1': {'styloid_1': 0.02, 'head_1': 0.02, 'epiphysis_1': 0.05,
|
||||
'metaphysis_1': 0.03, 'diaphysis_1': 0.8},
|
||||
'2': {'styloid_2': 0.04, 'epiphysis_2': 0.13, 'metaphysis_2': 0.03,
|
||||
'diaphysis_2': 0.8},
|
||||
'3': {'styloid_3': 0.04, 'epiphysis_3': 0.13, 'metaphysis_3': 0.03,
|
||||
'diaphysis_3': 0.8}
|
||||
}
|
||||
|
||||
|
||||
def _convert_bboxes(bboxes: Tensor, orig_w: int, orig_h: int,
|
||||
transformed_w=256, transformed_h=256) -> Tensor:
|
||||
"""Масштабирует координаты боксов обратно к
|
||||
размерам исходного изображения."""
|
||||
bboxes[:, [0, 2]] *= orig_w / transformed_w
|
||||
bboxes[:, [1, 3]] *= orig_h / transformed_h
|
||||
return bboxes
|
||||
|
||||
|
||||
def _get_bone_parts(box: Tensor, bone_type: str) -> dict[str: Tensor]:
|
||||
"""Разбивает бокc кости на сегменты согласно заданным пропорциям."""
|
||||
if bone_type == '4':
|
||||
return {'hand_4': box}
|
||||
xmin, ymin, xmax = box[:3]
|
||||
bone_h = (xmax - xmin) * (9 if bone_type == '1' else 5)
|
||||
parts, current_y = {}, ymin
|
||||
for part, ratio in bone_ratios[bone_type].items():
|
||||
part_h = ratio * bone_h
|
||||
parts[part] = (xmin, current_y, xmax, current_y + part_h)
|
||||
current_y = current_y + part_h
|
||||
return parts
|
||||
|
||||
|
||||
def _check_is_right(image: Tensor) -> bool:
|
||||
"""Определяет сторону (латеральность) по изображению."""
|
||||
image = transform_lr(image).unsqueeze(0).to(device)
|
||||
with torch.no_grad():
|
||||
outputs = model_lr(image)[0]
|
||||
return bool(torch.argmax(outputs).item())
|
||||
|
||||
|
||||
def _get_projection_bones(image: Tensor) -> Optional[ProjectionSegments]:
|
||||
"""Получает боксы и сегменты кости для проекции."""
|
||||
original = image.clone()
|
||||
image = transform_bone(image).unsqueeze(0).to(device)
|
||||
with torch.no_grad():
|
||||
outputs = model_bone(image)[0]
|
||||
scores = outputs['scores']
|
||||
|
||||
valid = scores >= 0.15
|
||||
bones_boxes = outputs['boxes'][valid].cpu()
|
||||
bone_labels = [str(int(i.item())) for i in outputs['labels'][valid].cpu()]
|
||||
filtered_scores = scores[valid]
|
||||
|
||||
if not any(filtered_scores):
|
||||
raise structs.ImagesError("Снимок иной анатомической области или низкого диагностического качества")
|
||||
|
||||
best = {}
|
||||
for score, box, label in zip(filtered_scores, bones_boxes, bone_labels):
|
||||
if label not in best or score.item() > best[label][0]:
|
||||
best[label] = (score.item(), box)
|
||||
# 4 - ладонь
|
||||
if '3' in best:
|
||||
best['4'] = best['3']
|
||||
best.pop('3')
|
||||
bones_boxes = torch.stack([b for _, b in best.values()])
|
||||
bone_labels = list(best.keys())
|
||||
conv_bone_boxes = _convert_bboxes(bones_boxes, original.shape[2],
|
||||
original.shape[1])
|
||||
|
||||
parts_boxes, parts_labels = [], []
|
||||
for box, bone in zip(conv_bone_boxes, bone_labels):
|
||||
segments = _get_bone_parts(box.cpu().numpy(), bone)
|
||||
for part, coords in segments.items():
|
||||
parts_boxes.append(torch.tensor(coords))
|
||||
parts_labels.append(part)
|
||||
parts_boxes = torch.stack(parts_boxes)
|
||||
|
||||
return ProjectionSegments(parts_boxes, parts_labels, conv_bone_boxes,
|
||||
bone_labels)
|
||||
|
||||
|
||||
def _get_fractions(direct_img: Tensor,
|
||||
score_threshold=0.35) -> tuple[Tensor, Tensor]:
|
||||
image = transform_frac(direct_img).unsqueeze(0).to(device)
|
||||
with torch.no_grad():
|
||||
outputs = model_frac(image)[0]
|
||||
scores = outputs['scores'].cpu()
|
||||
valid = scores >= score_threshold
|
||||
boxes = outputs['boxes'][valid].cpu()
|
||||
scores = scores[valid]
|
||||
keep = ops.nms(boxes, scores, 0.2)
|
||||
boxes = boxes[keep]
|
||||
scores = scores[keep]
|
||||
converted_bboxes = _convert_bboxes(boxes, direct_img.shape[2],
|
||||
direct_img.shape[1],
|
||||
transformed_w=512,
|
||||
transformed_h=512)
|
||||
return converted_bboxes, scores
|
||||
|
||||
|
||||
def _assign_fracs_to_bones(frac_boxes: Tensor,
|
||||
bone_boxes: Tensor, bone_labels: list[str]) \
|
||||
-> dict[str: tuple[Tensor, list[str]]]:
|
||||
"""Относит каждый бокс ровно к одной кости"""
|
||||
assignments = {}
|
||||
new_boxes = []
|
||||
i = 1
|
||||
if len(bone_boxes) > 0:
|
||||
for frac_box in frac_boxes:
|
||||
inter_xmin = torch.max(frac_box[0], bone_boxes[:, 0])
|
||||
inter_ymin = torch.max(frac_box[1], bone_boxes[:, 1])
|
||||
inter_xmax = torch.min(frac_box[2], bone_boxes[:, 2])
|
||||
inter_ymax = torch.min(frac_box[3], bone_boxes[:, 3])
|
||||
|
||||
x_len = (inter_xmax - inter_xmin).clamp(min=0)
|
||||
y_len = (inter_ymax - inter_ymin).clamp(min=0)
|
||||
inter_area = x_len * y_len
|
||||
|
||||
best_idx = torch.argmax(inter_area)
|
||||
if inter_area[best_idx].item() > 0:
|
||||
bone = bone_labels[best_idx]
|
||||
if bone in assignments:
|
||||
assignments[bone] = (
|
||||
torch.cat([assignments[bone][0], frac_box.unsqueeze(0)]),
|
||||
assignments[bone][1] + [f'Находка {i}'])
|
||||
else:
|
||||
assignments[bone] = (frac_box.unsqueeze(0), [f'Находка {i}'])
|
||||
new_boxes.append(frac_box)
|
||||
|
||||
i += 1
|
||||
boxes_tensor = torch.stack(new_boxes) if new_boxes else torch.tensor([])
|
||||
else:
|
||||
boxes_tensor = torch.tensor([])
|
||||
return assignments, boxes_tensor
|
||||
|
||||
|
||||
def _assign_fracs_to_parts(frac_boxes: Tensor, frac_labels: list[str],
|
||||
part_boxes: Tensor,
|
||||
part_labels: np.ndarray[str]) -> list[str]:
|
||||
"""Для каждого перелома ищет 2 части кости с наибольшим пересечением"""
|
||||
result = []
|
||||
|
||||
for i in range(frac_boxes.shape[0]):
|
||||
frac_box = frac_boxes[i]
|
||||
intersections = []
|
||||
for j in range(part_boxes.shape[0]):
|
||||
part_box = part_boxes[j]
|
||||
inter_xmin = max(frac_box[0].item(), part_box[0].item())
|
||||
inter_ymin = max(frac_box[1].item(), part_box[1].item())
|
||||
inter_xmax = min(frac_box[2].item(), part_box[2].item())
|
||||
inter_ymax = min(frac_box[3].item(), part_box[3].item())
|
||||
|
||||
# Если пересечение существует, вычисляем площадь пересечения
|
||||
if inter_xmax > inter_xmin and inter_ymax > inter_ymin:
|
||||
area = (inter_xmax - inter_xmin) * (inter_ymax - inter_ymin)
|
||||
intersections.append((j, area))
|
||||
|
||||
# Сортируем найденные пересечения по площади в
|
||||
# порядке убывания и выбираем топ-2
|
||||
intersections.sort(key=lambda x: x[1], reverse=True)
|
||||
for j, _ in intersections[:2]:
|
||||
result.append(f'{part_labels[j]}_{frac_labels[i]}')
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def _format_single_fracture(label: str) -> tuple[str, str]:
|
||||
"""
|
||||
Формирует сообщение для одного найденного перелома.
|
||||
"""
|
||||
name, part = label.split('_')[:2]
|
||||
bone = bone_translator[part]
|
||||
msg = f'выявлен признак перелома {bone_parts_translator[name]} {bone}.'
|
||||
return ('', msg) if part == '3' else (msg, '')
|
||||
|
||||
|
||||
def _format_multiple_fractures(labels: list[str]) -> tuple[str, str, int, int]:
|
||||
"""
|
||||
Группирует найденные переломы по идентификатору и формирует сообщения
|
||||
для фронтальной и боковой проекций.
|
||||
"""
|
||||
finds = {}
|
||||
for label in labels:
|
||||
name, part, n = label.split('_')
|
||||
bone = bone_translator[part]
|
||||
finds.setdefault(n, []).append((bone_parts_translator[name], bone))
|
||||
|
||||
front_msgs, side_msgs = [], []
|
||||
front_count = side_count = 0
|
||||
for n in sorted(finds.keys()):
|
||||
parts = [x[0] for x in finds[n]]
|
||||
if 'метафиза' in parts and 'эпифиза' in parts:
|
||||
parts = ['метаэпифиза']
|
||||
find_text = f' {n}: перелом ' + ', '.join(parts) + f' {finds[n][0][1]}.'
|
||||
if finds[n][0][1] != 'лучевой или локтевой кости':
|
||||
front_msgs.append(find_text)
|
||||
front_count += 1
|
||||
else:
|
||||
side_msgs.append(find_text)
|
||||
side_count += 1
|
||||
|
||||
front_msg = (f'выявлены признаки {front_count} ' +
|
||||
('переломов' if front_count > 1 else 'перелома') +
|
||||
'. ' + ''.join(front_msgs)) if front_count else ''
|
||||
side_msg = (f'выявлены признаки {side_count} ' +
|
||||
('переломов' if side_count > 1 else 'перелома') +
|
||||
'. ' + ''.join(side_msgs)) if side_count else ''
|
||||
return front_msg, side_msg, front_count, side_count
|
||||
|
||||
|
||||
def _make_reports(labels: list[str], is_r: bool) -> str:
|
||||
"""
|
||||
Формирует текстовый отчёт по найденным переломам.
|
||||
"""
|
||||
|
||||
labels = list(set(labels))
|
||||
num_frac = len(labels)
|
||||
lr = 'правого' if is_r else 'левого'
|
||||
|
||||
# Отсутствие переломов
|
||||
if num_frac == 0:
|
||||
return (
|
||||
f'На рентгенограмме {lr} лучезапястного сустава '
|
||||
f'признаков перелома не выявлено.')
|
||||
|
||||
# Один найденный перелом
|
||||
if num_frac == 1:
|
||||
front_msg, side_msg = _format_single_fracture(labels[0])
|
||||
front_count = 1 if front_msg else 0
|
||||
else:
|
||||
(front_msg, side_msg,
|
||||
front_count, side_count) = _format_multiple_fractures(labels)
|
||||
|
||||
report_lines = []
|
||||
if front_count:
|
||||
report_lines.append(
|
||||
f'На рентгенограмме {lr} лучезапястного сустава '
|
||||
f'{front_msg}')
|
||||
|
||||
return ' '.join(report_lines).replace(' ', ' ')
|
||||
|
||||
|
||||
def _make_conclusion(labels: list[str]) -> str:
|
||||
num_frac = len(labels)
|
||||
if num_frac == 0:
|
||||
return 'Признаков перелома не выявлено.'
|
||||
finds = {}
|
||||
for label in labels:
|
||||
name, part = label.split('_')[:2]
|
||||
bone = bone_translator[part]
|
||||
part = bone_parts_translator[name]
|
||||
if bone in finds:
|
||||
finds[bone].add(part)
|
||||
else:
|
||||
finds[bone] = {part}
|
||||
find_texts = []
|
||||
for bone in finds:
|
||||
if 'метафиза' in finds[bone] and 'эпифиза' in finds[bone]:
|
||||
finds[bone].remove('метафиза')
|
||||
finds[bone].remove('эпифиза')
|
||||
finds[bone].add('метаэпифиза')
|
||||
|
||||
if bone != 'лучевой или локтевой кости' or (
|
||||
'лучевой кости' not in finds and
|
||||
'локтевой кости' not in finds):
|
||||
find_text = f'перелом '
|
||||
find_text += ', '.join(sorted(finds[bone])) + f' {bone}'
|
||||
find_texts.append(find_text)
|
||||
conclusion = '; '.join(find_texts)
|
||||
return conclusion.replace(' ', ' ').capitalize()
|
||||
|
||||
|
||||
def _process_fractures(direct_img: Tensor,
|
||||
combined: ProjectionSegments) -> tuple:
|
||||
"""Обрабатываем боксы переломов, сортируем их и распределяем по
|
||||
сегментам кости.
|
||||
"""
|
||||
frac_boxes, frac_scores = _get_fractions(direct_img)
|
||||
frac_labels = [f'Находка {i + 1}' for i in range(len(frac_boxes))]
|
||||
frac_boxes = frac_boxes[frac_boxes[:, 0].argsort()]
|
||||
|
||||
assigned, frac_boxes = _assign_fracs_to_bones(frac_boxes,
|
||||
combined.bones_boxes,
|
||||
combined.bones_labels)
|
||||
hurt_bones = []
|
||||
for bone_type, (cur_frac_boxes, cur_frac_labels) in assigned.items():
|
||||
if bone_type == '4':
|
||||
for label in cur_frac_labels:
|
||||
hurt_bones.append(f'hand_4_{label}')
|
||||
else:
|
||||
mask = [lbl.endswith(f'_{bone_type}')
|
||||
for lbl in combined.parts_labels]
|
||||
mask_labels = np.array(combined.parts_labels)[mask]
|
||||
hurt_bones += _assign_fracs_to_parts(cur_frac_boxes,
|
||||
cur_frac_labels,
|
||||
combined.parts_boxes[mask],
|
||||
mask_labels)
|
||||
return Fractures(frac_boxes, frac_scores, frac_labels, hurt_bones)
|
||||
|
||||
|
||||
def _get_move_confidence(image: Tensor) -> float:
|
||||
image = transform_move(image).unsqueeze(0).to(device)
|
||||
|
||||
with torch.no_grad():
|
||||
outputs = model_move(image)[0]
|
||||
if torch.argmax(outputs).item() == 1:
|
||||
return 0
|
||||
move_prob = torch.softmax(outputs, 0)[0]
|
||||
return float(move_prob.item())
|
||||
|
||||
|
||||
def _get_diastas_len(image: Tensor, parts: ProjectionSegments) -> int:
|
||||
if '2' not in parts.bones_labels:
|
||||
return 0
|
||||
luch_box = parts.bones_boxes[parts.bones_labels.index('2')]
|
||||
xmin, _, xmax = luch_box[:3]
|
||||
bone_w = xmax - xmin
|
||||
|
||||
move_prob = _get_move_confidence(image)
|
||||
|
||||
coef = 0.025
|
||||
move_len = (bone_w * move_prob * coef).item()
|
||||
move_len = min(40, move_len)
|
||||
|
||||
return round(move_len)
|
||||
|
||||
|
||||
def _visualize_detections(direct_img: Tensor, study_iuid: str,
|
||||
laterality: Optional[str]) -> structs.Prediction:
|
||||
"""Основная функция визуализации детекции с объединением боксов"""
|
||||
if laterality in ['R', 'П']:
|
||||
is_r = True
|
||||
elif laterality in ['L', 'Л']:
|
||||
is_r = False
|
||||
else:
|
||||
is_r = _check_is_right(direct_img)
|
||||
|
||||
bone_segments = _get_projection_bones(direct_img)
|
||||
fracs = _process_fractures(direct_img, bone_segments)
|
||||
|
||||
report = _make_reports(fracs.bones, is_r)
|
||||
conclusion = _make_conclusion(fracs.bones)
|
||||
diastasis_mm = _get_diastas_len(direct_img, bone_segments)
|
||||
diastasis_mm = diastasis_mm if fracs.labels else 0
|
||||
|
||||
img = cv2.cvtColor(direct_img[0].numpy(), cv2.COLOR_GRAY2RGB)
|
||||
|
||||
color = (255, 0, 0)
|
||||
font = cv2.FONT_HERSHEY_COMPLEX
|
||||
for box, label in zip(fracs.boxes.cpu().numpy().astype(int), fracs.labels):
|
||||
cv2.rectangle(img, box[:2], box[2:], color, 2)
|
||||
ytext = max(box[1] - 5, 0)
|
||||
cv2.putText(img, label, (box[0], ytext), font, 0.5, color, 1)
|
||||
|
||||
cv2.imwrite(f"client/static/{study_iuid}.png", img)
|
||||
|
||||
is_fractured = bool(len(fracs.boxes))
|
||||
if not is_fractured:
|
||||
overall_probability = 0
|
||||
else:
|
||||
overall_probability = round(float(max(fracs.scores)*100))
|
||||
|
||||
properties = {
|
||||
"Макс. величина диастаза отломков, мм": diastasis_mm
|
||||
}
|
||||
|
||||
return structs.Prediction(overall_probability, is_fractured,
|
||||
report, conclusion, img, properties)
|
||||
|
||||
|
||||
def predict(input: structs.PredictorInput) -> structs.Prediction:
|
||||
return _visualize_detections(input.image, input.study_uid,
|
||||
input.laterality)
|
||||
@@ -0,0 +1,86 @@
|
||||
from typing import NamedTuple
|
||||
import os
|
||||
import torch
|
||||
import numpy as np
|
||||
import pydicom
|
||||
from service.structs import PredictorInput, TagError, MetaTags
|
||||
|
||||
|
||||
class InputImage(NamedTuple):
|
||||
img: np.ndarray
|
||||
ww: int
|
||||
wc: int
|
||||
uid: str
|
||||
color_inversion: bool
|
||||
|
||||
|
||||
def _get_ww_wc(study_ww, study_wc):
|
||||
if type(study_ww) is pydicom.valuerep.DSfloat:
|
||||
return int(study_ww), int(study_wc)
|
||||
return int(study_ww[0]), int(study_wc[0])
|
||||
|
||||
|
||||
def _is_color_inverted(study):
|
||||
return study.PhotometricInterpretation == "MONOCHROME1"
|
||||
|
||||
|
||||
def _prep_img(img_pack: InputImage) -> torch.Tensor:
|
||||
img = img_pack.img.astype(np.float32)
|
||||
lower_bound = img_pack.wc - 0.5 * img_pack.ww
|
||||
upper_bound = img_pack.wc + 0.5 * img_pack.ww
|
||||
img = np.clip(img, lower_bound, upper_bound)
|
||||
img = ((img - lower_bound) / img_pack.ww) * 255
|
||||
img = img.astype(np.uint8)
|
||||
if img_pack.color_inversion:
|
||||
img = 255 - img
|
||||
return torch.from_numpy(img[None, ...])
|
||||
|
||||
|
||||
def _check_required_tags(study: pydicom.FileDataset, pathology: str):
|
||||
invalid_tags = []
|
||||
if not (hasattr(study, "PhotometricInterpretation") and
|
||||
study.PhotometricInterpretation in ("MONOCHROME1", "MONOCHROME2")):
|
||||
invalid_tags.append("PhotometricInterpretation")
|
||||
if pathology in ("shoulder", "wrist"):
|
||||
if not (hasattr(study, "PixelSpacing") or
|
||||
hasattr(study, "ImagerPixelSpacing")):
|
||||
invalid_tags.append("PixelSpacing/ImagerPixelSpacing")
|
||||
for tag in ("WindowWidth", "WindowCenter"):
|
||||
if not hasattr(study, tag) or getattr(study, tag) == "":
|
||||
invalid_tags.append(tag)
|
||||
if len(invalid_tags) == 1:
|
||||
raise TagError(f"DICOM тег {invalid_tags[0]} не заполнен, "\
|
||||
"либо имеет некорректное значение")
|
||||
elif len(invalid_tags) > 1:
|
||||
raise TagError(f"DICOM теги {", ".join(invalid_tags)} не заполнены, "\
|
||||
"либо имеют некорректные значения")
|
||||
|
||||
|
||||
def _get_meta_tags(study: pydicom.FileDataset):
|
||||
return MetaTags(study.StudyInstanceUID, study.SeriesInstanceUID,
|
||||
getattr(study, "PatientID", None),
|
||||
getattr(study, "AccessionNumber", None),
|
||||
getattr(study, "IssuerOfPatientID", None),
|
||||
getattr(study, "FillerOrderNumberImagingServiceRequest",
|
||||
None))
|
||||
|
||||
|
||||
def _get_px_size(study: pydicom.FileDataset):
|
||||
if hasattr(study, "PixelSpacing"):
|
||||
return study.PixelSpacing
|
||||
if hasattr(study, "ImagerPixelSpacing"):
|
||||
return study.ImagerPixelSpacing
|
||||
return None, None
|
||||
|
||||
|
||||
def prep_imgs(pathology: str, study_path: str) -> tuple[MetaTags, PredictorInput]:
|
||||
instance = pydicom.dcmread(study_path, force=True)
|
||||
_check_required_tags(instance, pathology)
|
||||
meta_tags = _get_meta_tags(instance)
|
||||
ww, wc = _get_ww_wc(instance.WindowWidth, instance.WindowCenter)
|
||||
clr_inverted = _is_color_inverted(instance)
|
||||
img = InputImage(instance.pixel_array, ww, wc, meta_tags.study_iuid, clr_inverted)
|
||||
pred_input = PredictorInput(meta_tags.study_iuid, _prep_img(img),
|
||||
getattr(instance, "ImageLaterality", None),
|
||||
*_get_px_size(instance))
|
||||
return meta_tags, pred_input
|
||||
@@ -0,0 +1,166 @@
|
||||
import os
|
||||
import importlib
|
||||
import base64
|
||||
from zipfile import ZipFile
|
||||
from datetime import datetime, timezone, timedelta
|
||||
from pydicom.dataset import Dataset, validate_file_meta
|
||||
from pydicom.uid import UID, generate_uid
|
||||
import numpy as np
|
||||
from service import sr_tags, preprocessor, structs
|
||||
|
||||
MODEL_VERSION = "1.0.0"
|
||||
|
||||
|
||||
def _gen_seriesIUID(orig_seriesIUID, model, sr=False):
|
||||
if len(orig_seriesIUID) > 56:
|
||||
orig_seriesIUID = orig_seriesIUID[:56]
|
||||
match model:
|
||||
case "sinus":
|
||||
model_id = 1208
|
||||
case "wrist":
|
||||
model_id = 1249
|
||||
case "shoulder":
|
||||
model_id = 1250
|
||||
return f"{orig_seriesIUID}.{model_id}.{'2' if sr else '1'}"
|
||||
|
||||
|
||||
def _to_sr_datetime(datetime):
|
||||
return datetime.strftime("%d.%m.%Y %H:%M")
|
||||
|
||||
|
||||
def to_iso8601(datetime):
|
||||
msk_tz = timezone(timedelta(hours=3), name="MSK")
|
||||
date = datetime.astimezone(msk_tz).isoformat("T", "milliseconds")
|
||||
rindex_colon = date.rindex(':')
|
||||
return date[:rindex_colon] + date[rindex_colon+1:]
|
||||
|
||||
|
||||
def _create_ds_base(meta_tags: structs.MetaTags, sop_class_uid: UID):
|
||||
meta_info = Dataset()
|
||||
sop_instance_uid = generate_uid()
|
||||
meta_info.MediaStorageSOPClassUID = sop_class_uid
|
||||
meta_info.MediaStorageSOPInstanceUID = sop_instance_uid
|
||||
meta_info.TransferSyntaxUID = UID('1.2.840.10008.1.2')
|
||||
|
||||
ds = Dataset()
|
||||
ds.file_meta = meta_info
|
||||
validate_file_meta(ds.file_meta, enforce_standard=True)
|
||||
|
||||
ds.SOPClassUID = sop_class_uid
|
||||
ds.InstitutionName = "LORKT"
|
||||
ds.StudyInstanceUID = meta_tags.study_iuid
|
||||
ds.SOPInstanceUID = sop_instance_uid
|
||||
if meta_tags.patient_id:
|
||||
ds.PatientID = meta_tags.patient_id
|
||||
if meta_tags.accession_number:
|
||||
ds.AccessionNumber = meta_tags.accession_number
|
||||
if meta_tags.issuer_of_patient_id:
|
||||
ds.IssuerOfPatientID = meta_tags.issuer_of_patient_id
|
||||
if meta_tags.filler_number:
|
||||
ds.FillerOrderNumberImagingServiceRequest = meta_tags.filler_number
|
||||
return ds
|
||||
|
||||
|
||||
def _create_sr(meta_tags: structs.MetaTags, report: str, conclusion: str,
|
||||
process_end: datetime, model: str, username: str):
|
||||
sop_class_uid = UID('1.2.840.10008.5.1.4.1.1.88.33')
|
||||
ds = _create_ds_base(meta_tags, sop_class_uid)
|
||||
ds.SeriesInstanceUID = _gen_seriesIUID(meta_tags.series_iuid, model,
|
||||
sr=True)
|
||||
ds.Modality = "SR"
|
||||
ds.InstanceNumber = 1
|
||||
|
||||
process_end = _to_sr_datetime(process_end)
|
||||
tags = sr_tags.tags_for_models[model]
|
||||
tags_and_texts = (
|
||||
("Модальность", "РГ"),
|
||||
("Область исследования", tags["Область исследования"]),
|
||||
("Идентификатор исследования", meta_tags.study_iuid),
|
||||
("Дата и время формирования заключения ИИ-сервисом", process_end),
|
||||
("Предупреждение", "Заключение подготовлено программным обеспечением "\
|
||||
"с применением технологий искусственного "\
|
||||
"интеллекта"),
|
||||
("Предупреждение", "В исследовательских целях"),
|
||||
("Наименование сервиса", "ЛОР КТ"),
|
||||
("Версия сервиса", MODEL_VERSION),
|
||||
("Назначение сервиса", tags["Назначение сервиса"]),
|
||||
("Технические данные", tags["Технические данные"]),
|
||||
("Описание", report),
|
||||
("Заключение", conclusion),
|
||||
("Руководство пользователя", tags["Руководство пользователя"])
|
||||
)
|
||||
|
||||
ds.SpecificCharacterSet = "ISO_IR 192"
|
||||
ds.add_new((0x0040, 0xa730), 'SQ',
|
||||
[Dataset() for _ in range(len(tags_and_texts))])
|
||||
seq = ds.ContentSequence
|
||||
|
||||
for i, (tag, text) in enumerate(tags_and_texts):
|
||||
seq[i].RelationshipType = "CONTAINS"
|
||||
seq[i].ValueType = "TEXT"
|
||||
seq[i].TextValue = text
|
||||
seq[i].add_new((0x0040, 0xa043), 'SQ', [Dataset()])
|
||||
name_seq = seq[i].ConceptNameCodeSequence[0]
|
||||
name_seq.CodeValue = "209001"
|
||||
name_seq.CodingSchemeDesignator = "99PMP"
|
||||
name_seq.CodeMeaning = tag
|
||||
|
||||
save_path = f"data/{username}/sr/{meta_tags.study_iuid}.dcm"
|
||||
ds.save_as(save_path, implicit_vr=True, little_endian=True)
|
||||
return save_path
|
||||
|
||||
|
||||
def _create_a_series(meta_tags: structs.MetaTags, img: np.ndarray,
|
||||
model: str, username: str):
|
||||
acquisition_date = datetime.now().strftime("%Y%m%d")
|
||||
acquisition_time = datetime.now().strftime("%H%M%S")
|
||||
series_uid = _gen_seriesIUID(meta_tags.series_iuid, model)
|
||||
sop_class_uid = UID('1.2.840.10008.5.1.4.1.1.7')
|
||||
ds = _create_ds_base(meta_tags, sop_class_uid)
|
||||
ds.SeriesInstanceUID = series_uid
|
||||
ds.InstanceNumber = 1
|
||||
ds.Modality = "DX"
|
||||
ds.SeriesDescription = "LORKT"
|
||||
ds.InstitutionName = "LORKT"
|
||||
ds.InstitutionalDepartmentName = MODEL_VERSION
|
||||
ds.AcquisitionDate = acquisition_date
|
||||
ds.AcquisitionTime = acquisition_time
|
||||
ds.OperatorsName = "AI"
|
||||
ds.PixelData = bytes(img)
|
||||
ds.Rows, ds.Columns = img.shape[:2]
|
||||
ds.BitsAllocated = 8
|
||||
ds.BitsStored = 8
|
||||
ds.HighBit = 7
|
||||
ds.SamplesPerPixel = 3
|
||||
ds.PhotometricInterpretation = "RGB"
|
||||
ds.PixelRepresentation = 0
|
||||
ds.PlanarConfiguration = 0
|
||||
save_path = f"data/{username}/additional_series/{meta_tags.study_iuid}.dcm"
|
||||
ds.save_as(save_path, implicit_vr=True, little_endian=True)
|
||||
return save_path
|
||||
|
||||
|
||||
def _zip_reports(paths: list[str], username: str):
|
||||
study_uid = os.path.split(paths[0])[1]
|
||||
with ZipFile(f"data/{username}/reports/{study_uid.replace('.dcm', '.zip')}", 'w') as z:
|
||||
for path in paths:
|
||||
dir, id = os.path.split(path)
|
||||
dir = os.path.split(dir)[1]
|
||||
arcname = os.path.join(dir, id)
|
||||
z.write(path, arcname)
|
||||
|
||||
|
||||
def make_reports(pathology: str, study_path: str,
|
||||
username: str) -> structs.Prediction:
|
||||
meta_tags, pred_input = preprocessor.prep_imgs(pathology, study_path)
|
||||
module = importlib.import_module("service.predictors." + pathology)
|
||||
predict_func = getattr(module, "predict")
|
||||
prediction = predict_func(pred_input)
|
||||
process_end = datetime.now()
|
||||
sr_path = _create_sr(meta_tags, prediction.report, prediction.conclusion,
|
||||
process_end, pathology, username)
|
||||
a_series_path = _create_a_series(meta_tags, prediction.image, pathology,
|
||||
username)
|
||||
_zip_reports([sr_path, a_series_path], username)
|
||||
prediction = prediction._replace(image=f"{meta_tags.study_iuid}.png")
|
||||
return prediction
|
||||
@@ -0,0 +1,12 @@
|
||||
from datetime import datetime
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class UserData(BaseModel):
|
||||
username: str
|
||||
password: str
|
||||
|
||||
|
||||
class TokenData(BaseModel):
|
||||
exp: datetime
|
||||
sub: str
|
||||
@@ -0,0 +1,33 @@
|
||||
SINUS_TAGS = {
|
||||
"Область исследования": "Голова",
|
||||
"Назначение сервиса": "Поиск признаков синусита в верхнечелюстных "\
|
||||
"и лобных пазухах",
|
||||
"Технические данные": "Количество срезов - 1",
|
||||
"Руководство пользователя": "Красным цветом "\
|
||||
"выделены патологические находки в пазухах"
|
||||
}
|
||||
|
||||
WRIST_TAGS = {
|
||||
"Область исследования": "ОДА",
|
||||
"Назначение сервиса": "Выявление рентгенографических признаков перелома "\
|
||||
"кости/костей, образующих лучезапястный сустав",
|
||||
"Технические данные": "Обработано 1 изображение в прямой и 1 изображение"\
|
||||
" в боковой проекции",
|
||||
"Руководство пользователя": "Красным прямоугольником выделяется зона "\
|
||||
"перелома"
|
||||
}
|
||||
|
||||
SHOULDER_TAGS = {
|
||||
"Область исследования": "ОДА",
|
||||
"Назначение сервиса": "Выявление рентгенографических признаков перелома "\
|
||||
"кости/костей, образующих плечевой сустав",
|
||||
"Технические данные": "Обработано 1 изображение в прямой проекции",
|
||||
"Руководство пользователя": "Красным прямоугольником выделяется зона "\
|
||||
"перелома"
|
||||
}
|
||||
|
||||
tags_for_models = {
|
||||
"sinus": SINUS_TAGS,
|
||||
"wrist": WRIST_TAGS,
|
||||
"shoulder": SHOULDER_TAGS
|
||||
}
|
||||
@@ -0,0 +1,41 @@
|
||||
from typing import Optional, NamedTuple
|
||||
import torch
|
||||
import numpy as np
|
||||
|
||||
|
||||
class Prediction(NamedTuple):
|
||||
overall_probability: float
|
||||
is_pathology: bool
|
||||
report: str
|
||||
conclusion: str
|
||||
image: np.ndarray | str
|
||||
properties: dict
|
||||
|
||||
|
||||
class PredictorInput(NamedTuple):
|
||||
study_uid: str
|
||||
image: torch.Tensor
|
||||
laterality: Optional[str]
|
||||
px_width: Optional[float]
|
||||
px_height: Optional[float]
|
||||
|
||||
|
||||
class MetaTags(NamedTuple):
|
||||
study_iuid: str
|
||||
series_iuid: str
|
||||
patient_id: Optional[str]
|
||||
accession_number: Optional[str]
|
||||
issuer_of_patient_id: Optional[str]
|
||||
filler_number: Optional[str]
|
||||
|
||||
|
||||
class TagError(Exception):
|
||||
def __init__(self, msg):
|
||||
self.msg = msg
|
||||
super().__init__()
|
||||
|
||||
|
||||
class ImagesError(Exception):
|
||||
def __init__(self, msg):
|
||||
self.msg = msg
|
||||
super().__init__()
|
||||
Reference in New Issue
Block a user