import argparse import glob import json import os import sys import numpy as np import SimpleITK as sitk import torch from medpy import metric # Prefer pip-installed MONAI over the local monai/ folder. os.environ.setdefault("MONAI_SKIP_SUBMODULES", "1") _repo_root = os.path.abspath(os.path.dirname(__file__)) if "" in sys.path: sys.path.remove("") if _repo_root in sys.path: sys.path.remove(_repo_root) import monai # noqa: E402 sys.path.insert(0, _repo_root) from monai.utils import set_determinism from tqdm import tqdm from light_training.dataloading.dataset import MedicalDataset, get_train_val_test_loader_from_train set_determinism(123) 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): ## TC, WT and ET 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__": parser = argparse.ArgumentParser(description="Compute Dice/HD95 for BraTS2023 (TC/WT/ET) from saved predictions.") parser.add_argument("--pred_name", required=True, type=str, help="Prediction folder name under results_root.") parser.add_argument("--results_root", type=str, default="prediction_results") parser.add_argument("--data_dir", type=str, default="./data/fullres/train", help="Preprocessed data directory (contains *.npz).") parser.add_argument( "--gt_source", type=str, default="processed", choices=["processed", "raw"], help="GT source. 'processed' uses *_seg.npy from preprocessed dataset (recommended for /data/yty/brats23_processed). " "'raw' uses seg.nii.gz from --raw_data_dir.", ) parser.add_argument( "--raw_data_dir", type=str, default="./data/raw_data/BraTS2023/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData/", help="Raw BraTS2023 training data directory that contains case folders with seg.nii.gz.", ) parser.add_argument("--split", type=str, default="test", choices=["train", "val", "test", "all"]) parser.add_argument("--train_rate", type=float, default=0.7) parser.add_argument("--val_rate", type=float, default=0.1) parser.add_argument("--test_rate", type=float, default=0.2) parser.add_argument("--seed", type=int, default=42) parser.add_argument("--voxel_spacing", type=str, default="1,1,1", help="Voxel spacing for HD95, e.g. '1,1,1'.") args = parser.parse_args() voxel_spacing = [float(x) for x in args.voxel_spacing.split(",")] if args.split == "all": all_paths = sorted(glob.glob(os.path.join(args.data_dir, "*.npz"))) ds = MedicalDataset(all_paths, test=False) else: train_ds, val_ds, test_ds = get_train_val_test_loader_from_train( args.data_dir, train_rate=args.train_rate, val_rate=args.val_rate, test_rate=args.test_rate, seed=args.seed, ) ds = {"train": train_ds, "val": val_ds, "test": test_ds}[args.split] print(f"Evaluating {len(ds)} cases from split={args.split}") all_results = np.zeros((len(ds), 3, 2), dtype=np.float32) for ind, batch in enumerate(tqdm(ds, total=len(ds))): properties = batch["properties"] case_name = properties["name"] pred_path = os.path.join(args.results_root, args.pred_name, f"{case_name}.nii.gz") if not os.path.isfile(pred_path): raise FileNotFoundError(f"Prediction not found: {pred_path}") if args.gt_source == "raw": gt_path = os.path.join(args.raw_data_dir, case_name, "seg.nii.gz") if not os.path.isfile(gt_path): raise FileNotFoundError(f"GT not found: {gt_path}") gt_itk = sitk.ReadImage(gt_path) gt_array = sitk.GetArrayFromImage(gt_itk).astype(np.int32) gt_array = torch.from_numpy(gt_array) gt_array = convert_labels(gt_array).numpy() else: # preprocessed GT (same space as saved predictions from 4_predict.py) if "seg" not in batch: raise KeyError("gt_source=processed requires 'seg' in dataset samples, but it's missing.") seg = batch["seg"] # expected shape: (1, D, H, W) if isinstance(seg, np.ndarray): seg_t = torch.from_numpy(seg) else: # np.memmap is also an ndarray subclass, keep it generic seg_t = torch.from_numpy(np.asarray(seg)) if seg_t.ndim == 4 and seg_t.shape[0] == 1: seg_t = seg_t[0] gt_array = convert_labels(seg_t).numpy() pred_itk = sitk.ReadImage(pred_path) pred_array = sitk.GetArrayFromImage(pred_itk) m = each_cases_metric(gt_array, pred_array, voxel_spacing) all_results[ind, ...] = m out_dir = os.path.join(args.results_root, "result_metrics") os.makedirs(out_dir, exist_ok=True) out_path = os.path.join(out_dir, f"{args.pred_name}.npy") np.save(out_path, all_results) result = np.load(out_path) mean_per_class = result.mean(axis=0) std_per_class = result.std(axis=0) mean_dice = float(mean_per_class[:, 0].mean()) mean_hd95 = float(mean_per_class[:, 1].mean()) summary = { "pred_name": args.pred_name, "results_root": args.results_root, "data_dir": args.data_dir, "split": args.split, "gt_source": args.gt_source, "raw_data_dir": args.raw_data_dir if args.gt_source == "raw" else None, "voxel_spacing": voxel_spacing, "num_cases": int(result.shape[0]), "mean_per_class": mean_per_class.tolist(), # [TC, WT, ET] x [dice, hd95] "std_per_class": std_per_class.tolist(), "mean_dice": mean_dice, "mean_hd95": mean_hd95, } summary_path = os.path.join(out_dir, f"{args.pred_name}_summary.json") with open(summary_path, "w") as f: json.dump(summary, f, indent=2) print("saved:", out_path) print("summary:", summary_path) print(result.shape) print("mean(TC/WT/ET) [dice, hd95]:") print(mean_per_class) print("std(TC/WT/ET) [dice, hd95]:") print(std_per_class) print("mean dice:", mean_dice) print("mean hd95:", mean_hd95)