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Medical SAM3 is a foundation model for universal prompt-driven medical image segmentation, obtained by fully fine-tuning SAM3 on large-scale, heterogeneous 2D and 3D medical imaging datasets with paired segmentation masks and text prompts.

This U‑Net model masks features in Atomic Force Microscopy (AFM) height maps. It outputs a probability mask image, the same size as the raw AFM image; the accompanying python package, afMLevel code then applies a threshold (typically 0.5) to produce a binary mask.

ONNX export of the Cellpose cpsam (Cellpose-SAM) model for cell segmentation in microscopy images.

Semantic cell classification built on top of Cellpose, with a whole slide image (WSI) workflow and a QuPath extension for integrated inference and import.

Matěj Pekár, Vít Musil, Rudolf Nenutil, Petr Holub, Tomáš Brázdil

Segment Anything in 3D Medical Images and Videos

This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Hugging Face Space (available now): https://huggingface.co/spaces/Tournesol-Saturday/railNet-tooth-segmentation-in-CBCT-image - Code: https://github.com/Tournesol-Saturday/RAIL - Paper: RAIL: Region-Aware…

Pytorch Implementation of the paper: "MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day"

[OTO–HNS2024] A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery Ameen Amanian, Aseem Jain, Yuliang Xiao, Chanha Kim, Andy S. Ding, Manish Sahu, Russell Taylor, Mathias Unberath, Bryan K. Ward, Deepa Galaiya, Masaru Ishii, Francis X.

Pre-trained weights and exported models for our spine segmentation project. The source code, designed to reproduce our test results and facilitate training and running inference on your own data, is available on GitHub: https://github.com/MMIV-ML/fastMONAI/tree/master/research

I present a demo showcasing retinal vessel segmentation using the U-Net model, which is a well-known and widely used model in medical image segmentation. The model was trained on the DRIVE dataset, and the training process was conducted on Google Colab.