DCMQI: An open source library for standardized communication of quantitative image analysis results using DICOM

Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi

Kitware/trame: v3.2.6

Fix get_server: Enable decorator like usage of the method (c47f5fa) Documentation website: Improve responsiveness (dfd4c6c) examples: Better formatting (b69f286) discussion: Add example for 342 (7df9327) examples: Fix links on gallery (a337c85) website: Update guides (533dce1) Improve doc (427f79f) events: Update core features (8617dc1) examples: The basics (0b48852) website: Add more basic examples (d18860e) website: Fix image path (3d77c93

Kitware/trame: v3.2.5

Fix readme: Links for PyPI (e57cb3e) Documentation readme: Update image links (47d205c) features: Add missing links (c156261) vitepress: Update content (a116c31) website: Migrate to vitepress (e8a8acf) vue3: Trame-components now support vue2 and 3 (ea5bbcf) issue: Add working code for 329 (641cd3e) discussion: Working example for 328 (9d7677f) v3: Update listing with vue3 router support (b634e2a) examples: Improve error validation one (e42ea8e) jupyter: More examples (b962d9e) jupyter: Update notebook (24964bc) panel: Provide parity example (8304056) readme: Add pypi badges (8c363c1) example: Add mutli server example in jupyter (4ee68a8) panel: Add example to compare with panel (12de48e

Computational Radiomics System to Decode the Radiographic Phenotype

Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop noninvasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics, a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung lesions. Source code, documentation, and examples are publicly available at www.radiomics.io. With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research. (C) 2017 AACR

QIICR/dcmqi: Latest (updated on 2023-10-20 13:10 UTC)

dcmqi (DICOM for Quantitative Imaging) is a free, open source library that can help with the conversion between imaging research formats and the standard DICOM representation for image analysis result

QIICR/dcmqi: v1.3.0

dcmqi (DICOM for Quantitative Imaging) is a free, open source C++ library for conversion between imaging research formats and the standard DICOM representation for image analysis result