A computer-based automated algorithm for assessing acinar cell loss after experimental pancreatitis

Abstract

The change in exocrine mass is an important parameter to follow in experimental models of pancreatic injury and regeneration. However, at present, the quantitative assessment of exocrine content by histology is tedious and operatordependent, requiring manual assessment of acinar area on serial pancreatic sections. In this study, we utilized a novel computer-generated learning algorithm to construct an accurate and rapid method of quantifying acinar content. The algorithm works by learning differences in pixel characteristics from input examples provided by human experts. HE-stained pancreatic sections were obtained in mice recovering from a 2-day, hourly caerulein hyperstimulation model of experimental pancreatitis. For training data, a pathologist carefully outlined discrete regions of acinar and non-acinar tissue in 21 sections at various stages of pancreatic injury and recovery (termed the ''ground truth''). After the expert defined the ground truth, the computer was able to develop a prediction rule that was then applied to a unique set of high-resolution images in order to validate the process. For baseline, non-injured pancreatic sections, the software demonstrated close agreement with the ground truth in identifying baseline acinar tissue area with only a difference of 1%60.05% (p = 0.21). Within regions of injured tissue, the software reported a difference of 2.5%60.04% in acinar area compared with the pathologist (p = 0.47). Surprisingly, on detailed morphological examination, the discrepancy was primarily because the software outlined acini and excluded inter-acinar and luminal white space with greater precision. The findings suggest that the software will be of great potential benefit to both clinicians and researchers in quantifying pancreatic acinar cell flux in the injured and recovering pancreas.

A supervised learning framework for pancreatic islet segmentation with multi‐scale color‐texture features and rolling guidance filters

“…The work aims to identify the image patches from the images with labels, that is, it predicts the unknown instance (image patch) labels from the known bag (image) labels. A supervised learning segmentation method using pixel-wise classification has also been proposed and applied for detection of pancreatic acinar cells in histopathological images (18)(19)(20). The features in the classification are defined with the multi-scale intensity neighborhoods in each pixel.…”

“…Examples of superpixel generated with different methods. Original image is overlapped with ground-truth islet boundary in (a); superpixels generated from SLIC(19) and proposed method are shown in (b) and (c) respectively, where the boundaries of all the generated superpixels are overlapped on the original image. In both (b) and (c), an area containing islet boundary is magnified at the left-bottom of the image, in order to show the object adherent performances of different generation methods.…”