{"adoption": {"forks": 551, "observed_at": "2026-08-28T04:04:44.344286+00:00", "stars": 1441}, "canonical_url": "https://ross.abutalabs.com/products/pyradiomics", "card": {"archived": false, "artifact_type": "library", "description": "Open-source python package for the extraction of Radiomics features from 2D and 3D images and binary masks. Support: https://discourse.slicer.org/c/community/radiomics", "domain": ["healthcare", "data-science", "machine-learning"], "enriched": true, "function": ["image-processing", "data-science", "machine-learning"], "health_score": 49, "homepage": "http://pyradiomics.readthedocs.io/", "language": "Jupyter Notebook", "license": "BSD-3-Clause", "license_family": "permissive", "maturity": "active", "member_repos": ["AIM-Harvard/pyradiomics"], "name": "AIM-Harvard/pyradiomics", "platform": ["python", "cross-platform"], "pushed_at": "2025-10-27T22:04:28+00:00", "repo": "AIM-Harvard/pyradiomics", "stars": 1441, "tags": ["radiomics", "feature-extraction", "medical-imaging", "cancer-imaging", "ibsi", "simpleitk", "segmentation", "research", "docker"], "topics": ["radiomics", "cancer-imaging-research", "medical-imaging", "computational-imaging", "nci-qin", "tcia-dac", "python", "radiomics-features", "docker", "nci-itcr", "radiomics-feature-extraction", "feature-extraction", "ibsi"], "urls": [], "use_cases": ["extract radiomics features from medical images", "compute shape and texture features from tumor segmentations", "reproducible radiomic feature extraction for cancer research", "generate voxel-based feature maps from 3D scans", "apply wavelet and LoG filters before feature extraction", "quantify tumor phenotype from CT or MRI masks"], "what_it_is": "PyRadiomics is an open-source Python package for extracting radiomics features from 2D and 3D medical images and binary masks. It provides a tested, reproducible reference standard for radiomic analysis with segment-based and voxel-based feature computation.", "when_to_avoid": ["you need clinically validated software for patient care", "you need general-purpose image features for natural images", "you need deep-learning-based feature extraction rather than handcrafted features"], "when_to_choose": ["you need IBSI-compliant radiomic features in Python", "you want reproducible feature extraction with provenance info", "you work with 2D/3D medical images and binary masks", "you need a maintained reference standard for radiomics research"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/pyradiomics", "repo": "AIM-Harvard/pyradiomics", "role": "main", "score": 45}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:04:44.344286+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T04:36:26.440247+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "2589910ca46d9c2f67e3acb5822c1abad28d3555035934dd5468d681c72463a6", "fetched_at": "2026-08-28T04:04:44.344286+00:00", "kind": "readme", "missing": false, "url": "https://github.com/AIM-Harvard/pyradiomics"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 49, "longevity": 100, "rhythm": 8}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": [], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 4005, "days_push": 310, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 45, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}