https://github.com/amine0110/SAM-Medical-Imaging

https://github.com/amine0110/SAM-Medical-Imaging

Last synced: 5 months ago
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• Host: GitHub
• URL: https://github.com/amine0110/SAM-Medical-Imaging
• Owner: amine0110
• Created: 2023-04-07T17:58:50.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-04-14T16:17:50.000Z (about 2 years ago)
• Last Synced: 2024-08-03T23:24:01.945Z (9 months ago)
• Language: Jupyter Notebook
• Size: 1.45 MB
• Stars: 116
• Watchers: 3
• Forks: 27
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-segment-anything-extensions - Repo
• Awesome-Segment-Anything - Code - | SAM for Medical Imaging.| (Open Source Projects / Follow-up Papers)

README

        
# SAM Medical Imaging

[![YouTube](https://img.shields.io/badge/YouTube-%23FF0000.svg?style=for-the-badge&logo=YouTube&logoColor=white)](https://youtu.be/d4aRkCNG_iA) [![Website](https://img.shields.io/badge/WEBSITE-FFC800.svg?style=for-the-badge&logo=&logoColor=white)](https://pycad.co/sam-for-medical-imaging/) ![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54) ![PyTorch](https://img.shields.io/badge/PyTorch-%23EE4C2C.svg?style=for-the-badge&logo=PyTorch&logoColor=white)  [![Demo](https://img.shields.io/badge/Demo-0066ff.svg?style=for-the-badge&logo=&logoColor=)](https://segment-anything.com/) [![Instagram](https://img.shields.io/badge/Instagram-%23E4405F.svg?style=for-the-badge&logo=Instagram&logoColor=white)](https://www.instagram.com/pycad_/) [![Facebook](https://img.shields.io/badge/Facebook-%231877F2.svg?style=for-the-badge&logo=Facebook&logoColor=white)](https://www.facebook.com/pycadd/)

The Medical SAM (Segment Anything Model) repository is a fork of the [original SAM repository](https://github.com/facebookresearch/segment-anything) with modifications to support object segmentation in medical imaging using DICOM files. The SAM model is a state-of-the-art object segmentation model that predicts object masks given prompts that indicate the desired object. This implementation uses SAM to efficiently produce high-quality masks from prompts for medical imaging tasks using DICOM files. It allows the user to provide box prompt via the SamPredictor class to predict masks for a given medical DICOM file.

![SAM-medical-imaging](https://user-images.githubusercontent.com/37108394/230678827-f53b684c-6bca-491f-9f67-8fd1cd642717.png)

## 📩 Newsletter

Stay up-to-date on the latest in computer vision and medical imaging! Subscribe to my newsletter now for insights and analysis on the cutting-edge developments in this exciting field.

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## 🆕 NEW 

Learn how to effectively manage and process DICOM files in Python with our comprehensive course, designed to equip you with the skills and knowledge you need to succeed.

https://www.learn.pycad.co/course/dicom-simplified