{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"}],"enrichment":{"capability":"Automated segmentation of anatomical structures in CT and MR medical images, identifying and labeling 117 classes in CT or 50 classes in MR scans using deep learning.","skillfed_tags":["medical-imaging","deep-learning","segmentation"],"use_cases":["Automated organ volume measurement for abdominal imaging reports and follow-up studies","Rapid vertebrae identification and labeling in spine imaging for surgical planning","Vessel segmentation (aorta, pulmonary artery) for diameter measurement and aneurysm screening","Research preprocessing: batch segmentation of large CT/MR cohorts for statistical analysis","Integration into clinical workflows via 3D Slicer extension or web API for real-time reporting","Body composition analysis: extracting muscle, fat, and bone measurements from imaging"],"what_it_does":"TotalSegmentator is a command-line and Python API tool that automatically identifies and labels anatomical structures in medical CT and MR images using neural networks trained on diverse scanner protocols and institutions. It outputs segmentation masks for 117 anatomical classes in CT scans or 50 in MR scans, plus specialized subtasks for organs, vessels, vertebrae, and pathologies. The package wraps nnUNetv2 and depends on PyTorch, SimpleITK, nibabel, and scikit-image for image I/O, processing, and visualization.\n\nIt is designed for research and clinical decision support (though not FDA-cleared as a standalone device). The tool runs on CPU or GPU across Ubuntu, Mac, and Windows. Input accepts Nifti files or DICOM folders; output is segmentation masks in standard medical image formats. The package is actively maintained, has no known vulnerabilities, and is permissively licensed.","worth_installing":"Yes, with conditions. Install if you need automated medical image segmentation for research or clinical support and have PyTorch and GPU resources available. The active maintenance, permissive license, zero known vulnerabilities, and broad anatomical coverage make it a solid choice. Avoid if you require FDA-cleared medical device certification or cannot accommodate the large dependency footprint and model download. CPU-only use is feasible but slow without the --fast flag."},"id":"totalsegmentator","links":{"html":"https://skillfed.io/packages/totalsegmentator","md":"https://skillfed.io/packages/totalsegmentator.md","pypi":"https://pypi.org/project/totalsegmentator/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-12","license_spdx":null,"license_treatment":"permissive","name":"TotalSegmentator","python_support":"supports_current","summary":"Robust segmentation of 117 classes in CT images."},"popularity":{"monthly_downloads":87292,"position":13805,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.18.0"}