학술논문

Automated Quantification of Pancreatic Steatosis in Biopsy Images using a Classification Based System
Document Type
Conference
Source
2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 2021 6th South-East Europe. :1-5 Sep, 2021
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Training
Pathology
Image segmentation
Computer vision
Machine learning algorithms
Social networking (online)
Biopsy
Pancreas Biopsy
Pancreatitis
Non-Alcoholic Fatty Pancreas
Digital Image Processing
Machine Learning
Computer Vision
Language
Abstract
Non-Alcoholic Fatty Pancreas Disease (NAFPD) is the most common pancreatic condition in adults and is usually associated with obesity and insulin resistance. It is a new medical term that indicates the development of pancreatic steatosis, which at an advanced stage leads to the irreversible replacement of acinar cells with fat droplets. Although increasing prevalence rates are recorded worldwide for this condition, it has been studied to a small extent due to the diagnostic limitations of noninvasive medical imaging methods. In recent years and with the development of modern computer vision systems, digital pathology through biopsy imaging systems has become the gold standard in modern clinical trials. The current work presents an automated diagnostic tool for measuring the fat ratio in pancreatic biopsy specimens. The automated analysis is performed on a set of 20 histological images using supervised machine learning algorithms. Its diagnostic performance presents a minimum fat quantification error of 0.23% compared to that obtained from human semi-quantitative estimates.