KibromBerihu/ai4elife
This data-centric AI repository implements a robust deep learning method (LFBNet) for fully automated tumor segmentation in whole-body [18]F-FDG PET/CT images.
This project offers a fully automated way to identify and measure tumor lesions in whole-body 18F-FDG PET/CT images, specifically for Diffuse Large B-cell lymphoma (DLBCL) patients. It takes 3D PET scans and automatically segments tumor regions, then calculates key clinical endpoints like surrogate Total Metabolic Active Tumor Volume (sTMTV) and surrogate maximum tumor dissemination (sDmax). This tool is designed for oncologists, radiologists, and clinical researchers who need to quickly and consistently evaluate tumor burden and spread.
Use this if you need an automated, consistent, and fast method to quantify tumor burden and dissemination from PET images in DLBCL patients, without relying on manual delineation.
Not ideal if your imaging data is not 18F-FDG PET/CT or if you need to segment tumors for cancer types other than DLBCL.
Stars
40
Forks
10
Language
Python
License
MIT
Category
Last pushed
Dec 12, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/KibromBerihu/ai4elife"
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...