Supervised artificial intelligence (AI) methods for evaluation of medical images require a curation process for data to optimally train, validate, and test algorithms. The chief obstacles to develo...

https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020192224

Preparing Medical Imaging Data for Machine Learning.

Combo loss: Handling input and output imbalance in multi-organ segmentation

Background:De-identification and anonymization are strategies that are used to remove patient identifiers in electronic health record data. The use of these strategies in multicenter research studies is paramount in importance, given the need to share electronic health record data across multiple en

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Strategies for de-identification and anonymization of electronic health record data for use in multicenter research studies.

Radiotherapy (RT) is one of the basic treatment modalities for cancer of the head and neck (HN OAR - the spinal cord, brainstem, and major salivary glands are the most studied OARs, but additional experiments should be conducted for several less studied soft tissue structures; image database - several image databases with the corresponding ground truth are currently available for methodology evaluation, but should be augmented with data from multiple observers and multiple institutions; methodology - current methods have shifted from atlas-based to deep learning auto-segmentation, which is expected to become even more sophisticated; ground truth - delineation guidelines should be followed and participation of multiple experts from multiple institutions is recommended; performance metrics - the Dice coefficient as the standard volumetric overlap metrics should be accompanied with at least one distance metrics, and combined with clinical acceptability scores and risk assessments; segmentation performance - the best performing methods achieve clinically acceptable auto-segmentation for several OARs, however, the dosimetric impact should be also studied to provide clinically relevant endpoints for RT planning.

Auto-segmentation of organs at risk for head and neck radiotherapy planning: From atlas-based to deep learning methods.

Implementing the DICOM Standard for Digital Pathology.

Background. Imaging biomarkers hold tremendous promise for precision medicine clinical applications. Development of such biomarkers relies heavily on image post-processing tools for automated image quantitation. Their deployment in the context of clinical research necessitates interoperability with the clinical systems. Comparison with the established outcomes and evaluation tasks motivate integration of the clinical and imaging data, and the use of standardized approaches to support annotation and sharing of the analysis results and semantics. We developed the methodology and tools to support these tasks in Positron Emission Tomography and Computed Tomography (PET/CT) quantitative imaging (QI) biomarker development applied to head and neck cancer (HNC) treatment response assessment, using the Digital Imaging and Communications in Medicine (DICOM(®)) international standard and free open-source software. Methods. Quantitative analysis of PET/CT imaging data collected on patients undergoing treatment for HNC was conducted. Processing steps included Standardized Uptake Value (SUV) normalization of the images, segmentation of the tumor using manual and semi-automatic approaches, automatic segmentation of the reference regions, and extraction of the volumetric segmentation-based measurements. Suitable components of the DICOM standard were identified to model the various types of data produced by the analysis. A developer toolkit of conversion routines and an Application Programming Interface (API) were contributed and applied to create a standards-based representation of the data. Results. DICOM Real World Value Mapping, Segmentation and Structured Reporting objects were utilized for standards-compliant representation of the PET/CT QI analysis results and relevant clinical data. A number of correction proposals to the standard were developed. The open-source DICOM toolkit (DCMTK) was improved to simplify the task of DICOM encoding by introducing new API abstractions. Conversion and visualization tools utilizing this toolkit were developed. The encoded objects were validated for consistency and interoperability. The resulting dataset was deposited in the QIN-HEADNECK collection of The Cancer Imaging Archive (TCIA). Supporting tools for data analysis and DICOM conversion were made available as free open-source software. Discussion. We presented a detailed investigation of the development and application of the DICOM model, as well as the supporting open-source tools and toolkits, to accommodate representation of the research data in QI biomarker development. We demonstrated that the DICOM standard can be used to represent the types of data relevant in HNC QI biomarker development, and encode their complex relationships. The resulting annotated objects are amenable to data mining applications, and are interoperable with a variety of systems that support the DICOM standard.

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research.

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://githubcom/qiicr/dcmqi Cancer Res; 77(21); e87-90 ©2017 AACR

/pdf/dcmqi-an-open-source-library-for-standardized-communication-3ioyoepib7.pdf

dcmqi : An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM.

PURPOSEWe summarize Quantitative Imaging Informatics for Cancer Research (QIICR; U24 CA180918), one of the first projects funded by the National Cancer Institute (NCI) Informatics Technology for Ca...

Quantitative Imaging Informatics for Cancer Research.

Many real-world applications in the area of medical imaging like case study databases require separation of identify ing (IDATA) and non-identifying (MDATA) data, specifically those offering Internet-based data access. These kinds of projects also must provide a role-based access system, controlling, how patient data must be organized and how it can be accessed. On DICOM image level , different image types support different kind of information, intermixing IDATA and MDATA in a single object. To separate them, it is possible to reversibly anonymize DICOM objects by substituting IDATA by a unique anonymous token. In case that later an authenticated user needs full access to an image, this token can be used for re-linking formerly separated IDATA and MDATA, thus resulting in a dynamically generated, exact copy of the original image. The approach described in this paper is based on the automatic generation of anonymization policies from the DICOM standard text, providing specific support for all kinds of DICOM images. The policies are executed by a newly developed framework based on the DICOM toolkit DCMTK and offer a reliable approach to reversible anonymization. The implementation is evaluated in a German BMBF-supported expert network in the area of skeletal dysplasias, SKELNET, but may generall y be applicable to related projects, enormously improving quality and integrity of diagnostics in a field focused on images. It performs effectively and efficiently on real-world test images from the project and other kind of DICOM images.

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Jörg Riesmeier

Papers

DICOM for quantitative imaging biomarker development: a standards based approach to sharing clinical data and structured PET/CT analysis results in head and neck cancer research.

dcmqi : An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM.

Quantitative Imaging Informatics for Cancer Research.

Reversible Anonymization of DICOM Images Using Automatically Generated Policies