pyCERR

Overview

pyCERR (Python Computational Environment for Radiological Research) is an open-source Python toolkit for radiomics feature extraction, treatment plan analysis, and AI-assisted auto-segmentation, developed at Memorial Sloan Kettering Cancer Center (MSK) under the Deasy laboratory. It provides a standardized computational environment for reproducible radiation therapy research including dose-volume analysis, image feature extraction, and integration with AI segmentation models.

Used by

PMID:41941260 — pyCERR is part of the ROBIN consortium’s Data Science and Informatics Architecture (DSIA) working group infrastructure; adopted at MSK (Deasy lab) for AI auto-segmentation and radiomics as part of a FAIR-data strategy targeting the NCI Cancer Research Data Commons; paired with XNAT for imaging informatics across consortium sites PMID:41941260.

Notes

Corpus-grown slug; not present in canonical ontology.
pyCERR is the Python successor to CERR (Computational Environment for Radiological Research), which was originally developed in MATLAB at MSK.
Within ROBIN, pyCERR serves as a shared computational substrate for cross-institutional radiomics standardization, supporting the CBCT WG’s harmonized acquisition and analysis protocols.
Integrates with the NCI Cancer Research Data Commons and XNAT for imaging data management, supporting FAIR (Findable, Accessible, Interoperable, Reusable) data principles across the 5 U54 ROBIN centers.

Sources

PMID:41941260

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