ChenLiu-1996/CUTS
[MICCAI 2024] CUTS: A Deep Learning and Topological Framework for Multigranular Unsupervised Medical Image Segmentation
This tool helps medical professionals automatically identify and outline specific structures or anomalies within medical images like MRIs, CT scans, or retinal scans without needing pre-labeled examples. You provide medical images, and it outputs segmented images where different regions of interest are highlighted. This is ideal for radiologists, ophthalmologists, or research scientists working with medical imaging data.
Use this if you need to precisely delineate anatomical structures or pathological areas in medical images and lack the extensive labeled datasets typically required for traditional segmentation methods.
Not ideal if you require highly precise, pixel-level segmentation on very specific, rare pathologies where even minimal error is unacceptable and expert human annotation is readily available.
Stars
41
Forks
4
Language
Python
License
—
Category
Last pushed
Feb 14, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ChenLiu-1996/CUTS"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
dipy/dipy
DIPY is the paragon 3D/4D+ medical imaging library in Python. Contains generic methods for...
Project-MONAI/MONAI
AI Toolkit for Healthcare Imaging
Project-MONAI/MONAILabel
MONAI Label is an intelligent open source image labeling and learning tool.
neuronets/nobrainer
A framework for developing neural network models for 3D image processing.
Project-MONAI/monai-deploy-app-sdk
MONAI Deploy App SDK offers a framework and associated tools to design, develop and verify...