Reverse Dependencies of MedPy

The following projects have a declared dependency on MedPy:

• body-organ-analysis — BOA is a tool for segmentation of CT scans developed by the SHIP-AI group at the Institute for Artificial Intelligence in Medicine (https://ship-ai.ikim.nrw/). Combining the TotalSegmentator and the Body Composition Analysis, this tool is capable of analyzing medical images and identifying the different structures within the human body, including bones, muscles, organs, and blood vessels.
• CartiMorph-nnUnet — nnU-Net with minor revisions tailored for the CartiMorph framework.
• ctunet — 3D UNet for CT segmentation with PyTorch
• DBSegment — A deep learnign based method to segment deep brain structures from T1w MRI
• deepclustering — no summary
• fuse-med-ml — A python framework accelerating ML based discovery in the medical field by encouraging code reuse. Batteries included :)
• geowatch — no summary
• headctools — A set of tools for preproccessing and performing brain segmentation and skull reconstruction on head CT images
• hiwenet — Histogram-weighted Networks for Feature Extraction and Advance Analysis in Neuroscience
• meddlr — Meddlr is a config-driven framework built to simplify ML-based medical image reconstruction and analysis.
• medetpy — med-met
• medImgProc — Medical Image Processing module for viewing and editing.
• medsegpy — MedSegPy is a framework for research on medical image segmentation.
• mrqy — MRQy is a quality assurance and checking tool for quantitative assessment of magnetic resonance imaging (MRI) data.
• msk-seg-util — Python package for MSK segmentation utilities
• neurofeatures — Generic feature extraction package for neuroimaging features (examples include: connectivity from rs-fmri, regional mean fractional anisotropy, structural covariance etc)
• nnunet — nnU-Net. Framework for out-of-the box biomedical image segmentation.
• nnunet-customized — nnU-Net. Framework for out-of-the box biomedical image segmentation.
• nnunet-inference-on-cpu-and-gpu — nnU-Net. Framework for out-of-the box biomedical image segmentation. Can do inference on both gpu(if cuda available) and cpu(if cuda not available)
• skm-tea — A package for interacting with, visualizing, and benchmarking the SKM-TEA dataset
• trans-utils — no summary
• workjets — A common using tools library for working efficiently.