{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"},{"label":"Graphics","url":"https://skillfed.io/packages/category/multimedia-graphics/2"}],"enrichment":{"capability":"Highdicom provides a high-level Python API for reading, writing, and manipulating DICOM medical image files, with particular focus on machine learning and computational analysis workflows.","skillfed_tags":["medical-imaging","dicom-processing","ml-data-prep"],"use_cases":["Prepare medical imaging datasets for machine learning by reading DICOM frames and applying standardized pixel transforms for computational analysis.","Store machine learning model outputs (segmentations, heatmaps, predictions) back into DICOM format for clinical workflow integration and archival.","Create structured DICOM reports containing numerical results and annotations from computational analyses or human review.","Convert single-frame DICOM images to multi-frame format or apply presentation state transformations for standardized display.","Extract and filter information from existing derived DICOM objects (segmentations, parametric maps) for downstream processing."],"what_it_does":"Highdicom is a pure Python library that sits on top of pydicom to simplify working with DICOM medical images in computational contexts. It handles three main workflows: reading DICOM files of various modalities (radiology, pathology, etc.) and preparing their frames for analysis, including spatial arrangement and pixel transforms that pydicom does not handle; writing derived DICOM objects such as annotations, segmentations, parametric maps, structured reports, and secondary captures; and reading and filtering those derived DICOM files to extract stored information.\n\nThe package targets machine learning, computer vision, and similar computational analyses where standard DICOM tooling leaves gaps. It depends on pydicom for core DICOM parsing, numpy and pillow for image handling, pyjpegls for JPEG-LS codec support, typing-extensions for type hints, and packaging for version management. It requires Python 3.10 or later and installs as a pure wheel with no compiled dependencies.","worth_installing":"Yes. Highdicom is actively maintained, permissively licensed (MIT), has low install friction, and fills a genuine gap in DICOM tooling for computational workflows. It is well-supported by the imaging research community (NCI Imaging Data Commons, QIICR, Radiomics) and has no known security vulnerabilities. Install it if you work with DICOM files in machine learning or computational analysis contexts."},"id":"highdicom","links":{"html":"https://skillfed.io/packages/highdicom","md":"https://skillfed.io/packages/highdicom.md","pypi":"https://pypi.org/project/highdicom/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-28","license_spdx":"MIT","license_treatment":"permissive","name":"highdicom","python_support":"supports_current","summary":"High-level DICOM abstractions."},"popularity":{"monthly_downloads":83636,"position":14060,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.28.1"}