frankkramer-lab/MIScnn
A framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
This project helps medical imaging researchers and clinicians automatically identify and segment anatomical structures or anomalies like tumors in medical scans. You provide raw 2D or 3D medical images (like CT or MRI scans), and it outputs segmented images that highlight specific regions of interest. It's designed for biomedical engineers, radiologists, and medical researchers working with large datasets of medical images.
419 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to quickly set up and experiment with deep learning models for segmenting medical images, offering predefined architectures and robust data handling.
Not ideal if you need to perform general image processing tasks outside of medical image segmentation or require highly customized, non-deep learning approaches.
Stars
419
Forks
118
Language
Python
License
GPL-3.0
Category
Last pushed
May 10, 2023
Commits (30d)
0
Dependencies
12
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