| --- |
| license: apache-2.0 |
| tags: |
| - 3D medical |
| - referring expression segmentation |
| size_categories: |
| - n<1K |
| --- |
| |
| ## Dataset Description |
| 3D Medical Image Referring Segmentation Dataset (M3D-RefSeg), |
| consisting of 210 3D images, 2,778 masks, and text annotations. |
|
|
| ### Dataset Introduction |
| 3D medical segmentation is one of the main challenges in medical image analysis. In practical applications, |
| a more meaningful task is referring segmentation, |
| where the model can segment the corresponding region based on given text descriptions. |
| However, referring segmentation requires image-mask-text triplets, and the annotation cost is extremely high, |
| limiting the development of referring segmentation tasks in 3D medical scenarios. |
| To address this issue, we selected 210 images as a subset from the existing TotalSegmentator segmentation dataset |
| and re-annotated the text and corresponding regions. |
| Each image corresponds to multiple text descriptions of disease abnormalities and region annotations. |
| Experienced doctors conducted annotations, |
| with the original text in Chinese stored in the text_zh.txt file. |
| We used the Qwen 72B large language model for automatic translation, |
| saving the translated and organized English annotations to text.json. |
| Furthermore, we used a large language model to convert region description text into question-answer pairs, |
| saved in CSV files. |
| For referring expression segmentation code, please refer to [M3D](https://github.com/BAAI-DCAI/M3D). |
| |
| |
| |
| ### Supported Tasks |
| The data in this dataset can be represented in the form of image-mask-text, |
| where masks can be converted into box coordinates through bounding boxes. |
| |
| Supported tasks include: |
| - **3D Segmentation**: Text-guided segmentation, referring segmentation, inference segmentation, etc. |
| - **3D Positioning** Visual grounding/referring expression comprehension, referring expression generation. |
| |
| ## Dataset Format and Structure |
| |
| ### Data Format |
| <pre> |
| M3D_RefSeg/ |
| s0000/ |
| ct.nii.gz |
| mask.nii.gz |
| text.json |
| text_zh.txt |
| s0000/ |
| ...... |
| </pre> |
| |
| ### Dataset Download |
| #### Clone with HTTP |
| ```bash |
| git clone https://huggingface.co/datasets/GoodBaiBai88/M3D-RefSeg |
| ``` |
|
|
| #### SDK Download |
| ```bash |
| from datasets import load_dataset |
| dataset = load_dataset("GoodBaiBai88/M3D-RefSeg") |
| ``` |
|
|
| #### Manual Download |
| Download the files directly from the dataset repository. |
|
|
|
|
| ### Dataset Loading Method |
| #### 1. Preprocessing |
| After downloading the dataset, it needs to be processed using m3d_refseg_data_prepare.py, |
| including converting to a unified `npy` format, normalization, cropping, etc. |
| |
| #### 2. Build Dataset |
| We provide an example code for constructing the Dataset. |
| |
| ```python |
| class RefSegDataset(Dataset): |
| def __init__(self, args, tokenizer, mode="train"): |
| self.args = args |
| self.tokenizer = tokenizer |
| self.mode = mode |
| |
| self.image_tokens = "<im_patch>" * args.proj_out_num |
|
|
| train_transform = mtf.Compose( |
| [ |
| mtf.RandRotate90d(keys=["image", "seg"], prob=0.5, spatial_axes=(1, 2)), |
| mtf.RandFlipd(keys=["image", "seg"], prob=0.10, spatial_axis=0), |
| mtf.RandFlipd(keys=["image", "seg"], prob=0.10, spatial_axis=1), |
| mtf.RandFlipd(keys=["image", "seg"], prob=0.10, spatial_axis=2), |
| mtf.RandScaleIntensityd(keys="image", factors=0.1, prob=0.5), |
| mtf.RandShiftIntensityd(keys="image", offsets=0.1, prob=0.5), |
| mtf.ToTensord(keys=["image"], dtype=torch.float), |
| mtf.ToTensord(keys=["seg"], dtype=torch.int), |
| ] |
| ) |
| |
| val_transform = mtf.Compose( |
| [ |
| mtf.ToTensord(keys=["image"], dtype=torch.float), |
| mtf.ToTensord(keys=["seg"], dtype=torch.int), |
| ] |
| ) |
| set_track_meta(False) |
| |
| if mode == 'train': |
| self.data_list = pd.read_csv(args.refseg_data_train_path, engine='python') |
| self.transform = train_transform |
| elif mode == 'validation': |
| self.data_list = pd.read_csv(args.refseg_data_test_path, engine='python') |
| self.transform = val_transform |
| elif mode == 'test': |
| self.data_list = pd.read_csv(args.refseg_data_test_path, engine='python') |
| self.transform = val_transform |
| |
| def __len__(self): |
| return len(self.data_list) |
| |
| def __getitem__(self, idx): |
| max_attempts = 100 |
| for _ in range(max_attempts): |
| try: |
| data = self.data_list.iloc[idx] |
| image_path = os.path.join(self.args.data_root, data["Image"]) |
| |
| image_array = np.load(image_path) # 1*32*256*256, normalized |
| |
| seg_path = os.path.join(self.args.data_root, data["Mask"]) |
| seg_array = np.load(seg_path) |
| seg_array = (seg_array == data["Mask_ID"]).astype(np.int8) |
| |
| item = { |
| "image": image_array, |
| "seg": seg_array, |
| } |
| |
| it = self.transform(item) |
| |
| image = it['image'] |
| seg = it['seg'] # C*D*H*W |
| |
| question = data["Question"] |
| question = self.image_tokens + ' ' + question |
| |
| answer = data["Answer"] |
| |
| self.tokenizer.padding_side = "right" |
| text_tensor = self.tokenizer( |
| question + ' ' + answer, max_length=self.args.max_length, truncation=True, padding="max_length", return_tensors="pt" |
| ) |
| |
| input_id = text_tensor["input_ids"][0] |
| attention_mask = text_tensor["attention_mask"][0] |
| |
| valid_len = torch.sum(attention_mask) |
| if valid_len < len(input_id): |
| input_id[valid_len] = self.tokenizer.eos_token_id |
| |
| question_tensor = self.tokenizer( |
| question, max_length=self.args.max_length, truncation=True, padding="max_length", return_tensors="pt" |
| ) |
| question_len = torch.sum(question_tensor["attention_mask"][0]) |
| |
| label = input_id.clone() |
| label[label == self.tokenizer.pad_token_id] = -100 |
| label[:question_len] = -100 |
| |
| ret = { |
| 'image': image, |
| 'input_id': input_id, |
| 'label': label, |
| 'seg': seg, |
| 'attention_mask': attention_mask, |
| 'question': question, |
| 'answer': answer, |
| 'question_type': "refseg", |
| } |
| |
| return ret |
| |
| except Exception as e: |
| print(f"Error in __getitem__ at index {idx}: {e}") |
| idx = random.randint(0, len(self.data_list) - 1) |
| ``` |
| |
|
|
| ### Data Splitting |
| The dataset is divided by CSV files into: |
| - Training set: M3D_RefSeg_train.csv |
| - Test set: M3D_RefSeg_test.csv |
|
|
| ### Dataset Sources |
| This dataset is created from the open-source [TotalSegmentator](https://github.com/wasserth/TotalSegmentator). |
| For detailed information, please refer to TotalSegmentator. |
|
|
|
|
| ## Dataset Copyright Information |
|
|
| All data involved in this dataset are publicly available. |
|
|
|
|
| ## Citation |
| If our dataset and project are helpful to you, please cite the following work: |
|
|
| ```BibTeX |
| @misc{bai2024m3d, |
| title={M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models}, |
| author={Fan Bai and Yuxin Du and Tiejun Huang and Max Q. -H. Meng and Bo Zhao}, |
| year={2024}, |
| eprint={2404.00578}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| @misc{du2024segvol, |
| title={SegVol: Universal and Interactive Volumetric Medical Image Segmentation}, |
| author={Yuxin Du and Fan Bai and Tiejun Huang and Bo Zhao}, |
| year={2024}, |
| eprint={2311.13385}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| ``` |
