Developing image analysis pipelines of whole-slide images: Pre- and post-processing. Issue 1 (27th August 2020)

• Record Type: Journal Article
• Authors: Smith, Byron; Hermsen, Meyke; Lesser, Elizabeth; Ravichandar, Deepak; Kremers, Walter
• Is Part Of: Journal of clinical and translational science. Volume 5:Issue 1(2021)
• Publication Date: 2020-08-27
• Subjects: Image analysis, -- data science, -- analysis pipeline, -- deep learning, -- pathology, -- computer vision; Clinical medicine -- Research -- Periodicals; Medicine, Experimental -- Periodicals; Human experimentation in medicine -- Periodicals; 616.027
• DOI: 10.1017/cts.2020.531
• Languages: English
• ISSNs: 2059-8661

Abstract

Abstract: Deep learning has pushed the scope of digital pathology beyond simple digitization and telemedicine. The incorporation of these algorithms in routine workflow is on the horizon and maybe a disruptive technology, reducing processing time, and increasing detection of anomalies. While the newest computational methods enjoy much of the press, incorporating deep learning into standard laboratory workflow requires many more steps than simply training and testing a model. Image analysis using deep learning methods often requires substantial pre- and post-processing order to improve interpretation and prediction. Similar to any data processing pipeline, images must be prepared for modeling and the resultant predictions need further processing for interpretation. Examples include artifact detection, color normalization, image subsampling or tiling, removal of errant predictions, etc. Once processed, predictions are complicated by image file size – typically several gigabytes when unpacked. This forces images to be tiled, meaning that a series of subsamples from the whole-slide image (WSI) are used in modeling. Herein, we review many of these methods as they pertain to the analysis of biopsy slides and discuss the multitude of unique issues that are part of the analysis of very large images.