文章基本信息

标题：Ensemble Convolutional Neural Network Classification for Pancreatic Steatosis Assessment in Biopsy Images
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作者：Alexandros Arjmand ; Odysseas Tsakai ; Vasileios Christou 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2022
卷号：13
期号：4
页码：160
DOI：10.3390/info13040160
语种：English
出版社：MDPI Publishing
摘要：Non-alcoholic fatty pancreas disease (NAFPD) is a common and at the same time not extensively examined pathological condition that is significantly associated with obesity, metabolic syndrome, and insulin resistance. These factors can lead to the development of critical pathogens such as type-2 diabetes mellitus (T2DM), atherosclerosis, acute pancreatitis, and pancreatic cancer. Until recently, the diagnosis of NAFPD was based on noninvasive medical imaging methods and visual evaluations of microscopic histological samples. The present study focuses on the quantification of steatosis prevalence in pancreatic biopsy specimens with varying degrees of NAFPD. All quantification results are extracted using a methodology consisting of digital image processing and transfer learning in pretrained convolutional neural networks for the detection of histological fat structures. The proposed method is applied to 20 digitized histological samples, producing an 0.08% mean fat quantification error thanks to an ensemble CNN voting system and 83.3% mean Dice fat segmentation similarity compared to the semi-quantitative estimates of specialist physicians.