|
|
from light_training.dataloading.dataset import get_train_val_test_loader_from_train |
|
|
from monai.utils import set_determinism |
|
|
import torch |
|
|
import os |
|
|
import numpy as np |
|
|
import SimpleITK as sitk |
|
|
from medpy import metric |
|
|
import argparse |
|
|
from tqdm import tqdm |
|
|
|
|
|
import numpy as np |
|
|
|
|
|
set_determinism(123) |
|
|
|
|
|
parser = argparse.ArgumentParser() |
|
|
|
|
|
parser.add_argument("--pred_name", required=True, type=str) |
|
|
|
|
|
results_root = "prediction_results" |
|
|
args = parser.parse_args() |
|
|
|
|
|
pred_name = args.pred_name |
|
|
|
|
|
def cal_metric(gt, pred, voxel_spacing): |
|
|
if pred.sum() > 0 and gt.sum() > 0: |
|
|
dice = metric.binary.dc(pred, gt) |
|
|
hd95 = metric.binary.hd95(pred, gt, voxelspacing=voxel_spacing) |
|
|
return np.array([dice, hd95]) |
|
|
else: |
|
|
return np.array([0.0, 50]) |
|
|
|
|
|
def each_cases_metric(gt, pred, voxel_spacing): |
|
|
classes_num = 3 |
|
|
class_wise_metric = np.zeros((classes_num, 2)) |
|
|
for cls in range(0, classes_num): |
|
|
class_wise_metric[cls, ...] = cal_metric(pred[cls], gt[cls], voxel_spacing) |
|
|
print(class_wise_metric) |
|
|
return class_wise_metric |
|
|
|
|
|
def convert_labels(labels): |
|
|
|
|
|
labels = labels.unsqueeze(dim=0) |
|
|
|
|
|
result = [(labels == 1) | (labels == 3), (labels == 1) | (labels == 3) | (labels == 2), labels == 3] |
|
|
|
|
|
return torch.cat(result, dim=0).float() |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
data_dir = "./data/fullres/train" |
|
|
raw_data_dir = "./data/raw_data/BraTS2023/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/" |
|
|
train_ds, val_ds, test_ds = get_train_val_test_loader_from_train(data_dir) |
|
|
print(len(test_ds)) |
|
|
all_results = np.zeros((250,3,2)) |
|
|
|
|
|
ind = 0 |
|
|
for batch in tqdm(test_ds, total=len(test_ds)): |
|
|
properties = batch["properties"] |
|
|
case_name = properties["name"] |
|
|
gt_itk = os.path.join(raw_data_dir, case_name, f"seg.nii.gz") |
|
|
voxel_spacing = [1, 1, 1] |
|
|
gt_itk = sitk.ReadImage(gt_itk) |
|
|
gt_array = sitk.GetArrayFromImage(gt_itk).astype(np.int32) |
|
|
gt_array = torch.from_numpy(gt_array) |
|
|
gt_array = convert_labels(gt_array).numpy() |
|
|
pred_itk = sitk.ReadImage(f"./{results_root}/{pred_name}/{case_name}.nii.gz") |
|
|
pred_array = sitk.GetArrayFromImage(pred_itk) |
|
|
|
|
|
m = each_cases_metric(gt_array, pred_array, voxel_spacing) |
|
|
|
|
|
all_results[ind, ...] = m |
|
|
|
|
|
ind += 1 |
|
|
|
|
|
os.makedirs(f"./{results_root}/result_metrics/", exist_ok=True) |
|
|
np.save(f"./{results_root}/result_metrics/{pred_name}.npy", all_results) |
|
|
|
|
|
result = np.load(f"./{results_root}/result_metrics/{pred_name}.npy") |
|
|
print(result.shape) |
|
|
print(result.mean(axis=0)) |
|
|
print(result.std(axis=0)) |
|
|
|
|
|
|
|
|
|
|
|
|
