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