VISTA: VIsual Semantic Tissue Analysis for pancreatic disease quantification in murine cohorts.

ABSTRACT: Mechanistic disease progression studies using animal models require objective and quantifiable assessment of tissue pathology. Currently quantification relies heavily on staining methods which can be expensive, labor/time-intensive, inconsistent across laboratories and batch, and produce uneven staining that is prone to misinterpretation and investigator bias. We developed an automated semantic segmentation tool utilizing deep learning for rapid and objective quantification of histologic features relying solely on hematoxylin and eosin stained pancreatic tissue sections. The tool segments normal acinar structures, the ductal phenotype of acinar-to-ductal metaplasia (ADM), and dysplasia with Dice coefficients of 0.79, 0.70, and 0.79, respectively. To deal with inaccurate pixelwise manual an

SUBMITTER: Ternes L 

PROVIDER: S-EPMC7708430 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature