![]() ![]() Compared to the related areas of radiology and genomics, open-source tools for the management, visualization, and analysis of digital pathology has lagged. ![]() Digitization enables the application of computational image analysis and machine learning algorithms to characterize the contents of these images, and to understand the relationships between histology, clinical outcomes, and molecular data from genomic platforms. Improvements in imaging technology, decreases in storage costs, and regulatory approval of digital pathology for primary diagnosis have resulted in an explosion of whole-slide imaging data. Whole-slide imaging captures the histologic details of tissues in large multiresolution images. The functionality offered by HistomicsTK can be extended using slicer cli web which allows developers to integrate their image analysis algorithms into DSA for dissemination through HistomicsUI. ![]() It can function as a stand-alone library, or as a Digital Slide Archive plugin that allows users to invoke image analysis jobs through HistomicsUI. HistomicsTK is a Python package for the analysis of digital pathology images. ![]()
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