Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis

Journal article

AbstractThe importance of fibrillar collagen topology and organization in disease progression and prognostication in different types of cancer has been characterized extensively in many research studies. These explorations have either used specialized imaging approaches, such as specific stains (e.g., picrosirius red), or advanced and costly imaging modalities (e.g., second harmonic generation imaging (SHG)) that are not currently in the clinical workflow. To facilitate the analysis of stromal biomarkers in clinical workflows, it would be ideal to have technical approaches that can characterize fibrillar collagen on standard H&E stained slides produced during routine diagnostic work. Here, we present a machine learning-based stromal collagen image synthesis algorithm that can be incorporated into existing H&E-based histopathology workflow. Specifically, this solution applies a convolutional neural network (CNN) directly onto clinically standard H&E bright field images to extract information about collagen fiber arrangement and alignment, without requiring additional specialized imaging stains, systems or equipment.

Authors: Keikhosravi, A; Li, B; Liu, Y; Conklin, MW; Loeffler, AG

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Nature Research
• Journal: Communications Biology
• Volume: 3
• Issue: 1
• Pages: 414-414
• Publication date: 2020-07-31
• DOI: 10.1038/s42003-020-01151-5
• EISSN: 2399-3642
• ISSN: 2399-3642
• Language: English
• Keywords: Computer science; Convolutional neural network; Database; Workflow; Pattern recognition (psychology); Modality (human–computer interaction); Biomedical engineering; Collagen fiber; Artificial intelligence; Anatomy; Medicine; Histopathology; Stromal cell; Pathology