new branch: add HF load_dataset() support

.gitattributes

@@ -57,3 +57,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+*.nii.gz filter=lfs diff=lfs merge=lfs -text

LICENSE

@@ -0,0 +1 @@
+Data and code are released under CC-BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).

OAIZIB-CM.py

@@ -0,0 +1,108 @@
+import os
+import pandas as pd
+import nibabel as nib
+from datasets import GeneratorBasedBuilder, SplitGenerator, Split, DatasetInfo
+
+
+class OAIZIBCMDataset(GeneratorBasedBuilder):
+    VERSION = "1.0.0"
+
+    def _info(self):
+        return DatasetInfo(
+            description=(
+                "This is the official release of the OAIZIB-CM dataset.\n\n"
+                "OAIZIB-CM is based on the OAIZIB dataset.\n"
+                "OAIZIB paper: Automated Segmentation of Knee Bone and Cartilage combining Statistical Shape Knowledge "
+                "and Convolutional Neural Networks: Data from the Osteoarthritis Initiative.\n"
+                "In OAIZIB-CM, tibial cartilage is split into medial and lateral tibial cartilages.\n"
+                "OAIZIB-CM includes CLAIR-Knee-103R, consisting of:\n"
+                "  - a template image learned from 103 MR images of subjects without radiographic OA,\n"
+                "  - a corresponding 5-ROI segmentation mask for cartilages and bones, and\n"
+                "  - a corresponding 20-ROI atlas for articular cartilages.\n\n"
+                "Papers:\n"
+                "The dataset originates from these projects:\n"
+                " - CartiMorph: https://github.com/YongchengYAO/CartiMorph\n"
+                " - CartiMorph Toolbox: https://github.com/YongchengYAO/CartiMorph-Toolbox\n"
+                " - https://github.com/YongchengYAO/CMT-AMAI24paper\n\n"
+                "Please cite the following if you use the dataset:\n"
+                "\n@article{YAO2024103035,\n"
+                "title = {CartiMorph: A framework for automated knee articular cartilage morphometrics},\n"
+                "journal = {Medical Image Analysis},\n"
+                "author = {Yongcheng Yao and Junru Zhong and Liping Zhang and Sheheryar Khan and Weitian Chen},\n"
+                "volume = {91},\n"
+                "pages = {103035},\n"
+                "year = {2024},\n"
+                "issn = {1361-8415},\n"
+                "doi = {https://doi.org/10.1016/j.media.2023.103035}\n"
+                "}\n\n"
+                "@InProceedings{10.1007/978-3-031-82007-6_16,\n"
+                "author = {Yao, Yongcheng and Chen, Weitian},\n"
+                "editor = {Wu, Shandong and Shabestari, Behrouz and Xing, Lei},\n"
+                "title = {Quantifying Knee Cartilage Shape and Lesion: From Image to Metrics},\n"
+                "booktitle = {Applications of Medical Artificial Intelligence},\n"
+                "year = {2025},\n"
+                "publisher = {Springer Nature Switzerland},\n"
+                "address = {Cham},\n"
+                "pages = {162--172}\n"
+                "}\n\n"
+                "License:\n"
+                "This dataset is released under the CC BY-NC 4.0 license. It is compulsory to cite the above papers if you use the dataset.\n"
+            ),
+            features={
+                "image": "array",  # You can refine this by specifying shape/dtype if needed
+                "mask": "array",
+                "image_path": "string",
+                "mask_path": "string",
+            },
+        )
+
+    def _split_generators(self, dl_manager):
+        # Load the CSV files from the repository root.
+        train_df = pd.read_csv("train.csv")
+        test_df = pd.read_csv("test.csv")
+        return [
+            SplitGenerator(
+                name=Split.TRAIN, gen_kwargs={"df": train_df, "split": "train"}
+            ),
+            SplitGenerator(
+                name=Split.TEST, gen_kwargs={"df": test_df, "split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, df, split):
+        # Determine base directories based on the split
+        if split == "train":
+            base_img_dir = os.path.join("data", "imagesTr")
+            base_mask_dir = os.path.join("data", "labelsTr")
+        elif split == "test":
+            base_img_dir = os.path.join("data", "imagesTs")
+            base_mask_dir = os.path.join("data", "labelsTs")
+        else:
+            base_img_dir = ""
+            base_mask_dir = ""
+
+        # Iterate over each row in the CSV file; assume the CSV has 2 columns with file names.
+        for idx, row in df.iterrows():
+            # Extract file names from the first and second columns
+            img_file = row[0]
+            mask_file = row[1]
+            # Construct full paths
+            img_path = os.path.join(base_img_dir, img_file)
+            mask_path = os.path.join(base_mask_dir, mask_file)
+            try:
+                image = nib.load(img_path).get_fdata()
+            except Exception as e:
+                print(f"Error loading image from {img_path}: {e}")
+                continue
+            try:
+                mask = nib.load(mask_path).get_fdata()
+            except Exception as e:
+                print(f"Error loading mask from {mask_path}: {e}")
+                continue
+
+            yield idx, {
+                "image": image,
+                "mask": mask,
+                "image_path": img_path,
+                "mask_path": mask_path,
+            }

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