---
language:
- en
license: mit
tags:
- biology
- medical
- point cloud
- completion
task_categories:
- GRAPH_MACHINE_LEARNING
---

### MedPointS-CPL
This is the medical point cloud completion dataset from [MedPointS](https://flemme-docs.readthedocs.io/en/latest/medpoints.html), as presented in the paper "Hierarchical Feature Learning for Medical Point Clouds via State Space Model".

-   **Paper**: [Hierarchical Feature Learning for Medical Point Clouds via State Space Model](https://huggingface.co/papers/2504.13015)
-   **Code**: https://github.com/wlsdzyzl/flemme
-   **Project page**: https://flemme-docs.readthedocs.io/en/latest/medpoints.html

In this dataset, `partial` is the partial point cloud, 'target' is the target point cloud, and `label` is the class label.

Each point cloud has been normalized and sub-sampled to 2048 points. The correspondence between class names and labels is listed as follows (the label value plus 1 is the actual key of following map):

```
coarse_label_to_organ = {1: 'adrenalgland',
    2: 'aorta',
    3: 'autochthon',
    4: 'bladder',
    5: 'brain',
    6: 'breast',
    7: 'bronchie',
    8: 'celiactrunk',
    9: 'cheek',
    10: 'clavicle',
    11: 'colon',
    12: 'costa',
    13: 'duodenum',
    14: 'esophagus',
    15: 'eyeball',
    16: 'femur',
    17: 'gallbladder',
    18: 'gluteusmaximus',
    19: 'heart',
    20: 'hip',
    21: 'humerus',
    22: 'iliacartery',
    23: 'iliacvena',
    24: 'iliopsoas',
    25: 'inferiorvenacava',
    26: 'kidney',
    27: 'liver',
    28: 'lung',
    29: 'mediastinaltissue',
    30: 'pancreas',
    31: 'portalveinandsplenicvein',
    32: 'smallbowel',
    33: 'spleen',
    34: 'stomach',
    35: 'thymus',
    36: 'thyroid',
    37: 'trachea',
    38: 'uterocervix',
    39: 'uterus',
    40: 'vertebrae',
    41: 'gonads',
    42: 'sacrum',
    43: 'clavicula',
    # 44: 'prostate',
    44: 'pulmonaryartery',
    # 45: 'ribcartilage',
    45: 'rib',
    46: 'scapula',
    # 48: 'skull',
    # 49: 'spinalcanal',
    # 50: 'sternum'
    }
```

### Sample Usage

To train and evaluate models for point cloud completion using the Flemme framework, you can use the following commands. Note that you may need to adjust `/path/to/project/flemme/` to your local Flemme installation path.

```bash
## completion
train_flemme --config /path/to/project/flemme/resources/pcd/medpoints/cpl/train_pointmamba2knn_cpl.yaml
test_flemme --config /path/to/project/flemme/resources/pcd/medpoints/cpl/test_pointmamba2knn_cpl.yaml
```

### Citation
If you find our project helpful, please consider to cite the following work:

```
@misc{zhang2025hierarchicalfeaturelearningmedical,
      title={Hierarchical Feature Learning for Medical Point Clouds via State Space Model}, 
      author={Guoqing Zhang and Jingyun Yang and Yang Li},
      year={2025},
      eprint={2504.13015},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2504.13015}, 
}
```