A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images
Mahesh Anil Inamdar, U. Raghavendra, Anjan Gudigar, Sarvesh Bhandary, Massimo Salvi, Ravinesh C. Deo, Prabal Datta Barua, Edward J. Ciaccio, Filippo Molinari, U. Rajendra Acharya
Abstract
A foremost cause of death in males worldwide is prostate cancer. Its early identification, detection and diagnosis is crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopathology and deep learning. These methods have the potential to revolutionize the approach by identifying hidden patterns within the image. The recent improvements in data acquisition, processing and analysis of Deep Learning Models has made Artificial Intelligence driven Healthcare a topic of intensive investigation, in terms of inferring from the data and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. It focuses on developing new methods for segmenting these histopathological images using a multi-channel algorithm with an attention mechanism to focus on important areas. We compare our results with a host of contemporary techniques, and show that our method performs better at the segmentation task for histopathological imagery. Our method is able to delineate gland and background parts with an average Dice-coefficient of 0.9168. In this attention-based model, we have thereby demonstrated an accurate segmentation of glands regions, which could have significant positive implications for medical screening applications.