license: apache-2.0
base_model: blueyo0/SAM-Med3D
datasets:
- pcarnahan/MVSeg2023
pipeline_tag: image-segmentation
library_name: pytorch
tags:
- medical-imaging
- ultrasound
- echocardiography
- segmentation
- sam-med3d
- segment-anything
- 3d
- mitral-valve
model-index:
- name: UltraSAM-3D-Ultrasound-MVSeg
results:
- task:
type: image-segmentation
dataset:
name: MVSeg 2023
type: pcarnahan/MVSeg2023
metrics:
- type: dice
value: 0.87
name: Validation Dice
UltraSAM — SAM-Med3D fine-tuned for 3D ultrasound (mitral valve)
A fine-tuned SAM-Med3D checkpoint for promptable segmentation of the mitral valve in 3D echocardiography, trained on the MVSeg 2023 dataset with a boundary-aware objective.
🏆 Part of the UltraSAM project — 4th place at AI for Life 2026 Hackathon.
- Code, training & interactive MONAI Label app: https://github.com/thelet/UltraSAM-3D-Ultrasound-Segmentation
- Base model: SAM-Med3D (
vit_b_ori, 128³ input) - Task: interactive (click-prompted) binary segmentation
- Modality: 3D ultrasound / echocardiography
Results
Predicted mitral-valve segmentation on a held-out test case, shown in 3D Slicer. Pink = model prediction, yellow = ground truth. Top: 3D surface renderings; bottom: axial / sagittal / coronal slices with overlays.
Usage
import medim
model = medim.create_model(
"SAM-Med3D", pretrained=True,
checkpoint_path="sam_model_dice_best.pth",
)
See the GitHub repo
for the full inference pipeline (run_infer_no_baseline.py) and the interactive
3D Slicer workflow.
Training
Fine-tuned for 200 epochs on MVSeg 2023 with a combined DiceCE + 0.5·Boundary objective. Validation Dice plateaus around ~0.87.
| Train Dice | Validation Dice |
|---|---|
 |
 |
Attribution & license
This model is a fine-tuned derivative of SAM-Med3D (Apache-2.0) and is released under the same license. The base weights and architecture are the work of the SAM-Med3D authors; this repository modifies them by fine-tuning on 3D echocardiography (MVSeg 2023) with a boundary-aware objective.
- Base model: SAM-Med3D (Apache-2.0)
- SAM-Med3D builds on Meta AI's Segment Anything (Apache-2.0)
- Dataset: MVSeg 2023
Citation
@article{wang2023sammed3d,
title={SAM-Med3D},
author={Wang, Haoyu and Guo, Sizheng and Ye, Jin and Deng, Zhongying and
Cheng, Junlong and Li, Tianbin and Chen, Jianpin and Su, Yanzhou and
Huang, Ziyan and Shen, Yiqing and Fu, Bin and Zhang, Shaoting and
He, Junjun and Qiao, Yu},
journal={arXiv preprint arXiv:2310.15161},
year={2023}
}