A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images
Article
Inamdar, Mahesh Anil, Raghavendra, U., Gudigar, Anjan, Bhandary, Sarvesh, Salvi, Massimo, Deo, Ravinesh C., Barua, Prabal Datta, Ciaccio, Edward J., Molinari, Filippo and Acharya, RU. Rajendra. 2023. "A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images." IEEE Access. 11, pp. 108982-108994. https://doi.org/10.1109/ACCESS.2023.3321273
| Article Title | A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images |
|---|---|
| Article Category | Article |
| Authors | Inamdar, Mahesh Anil, Raghavendra, U., Gudigar, Anjan, Bhandary, Sarvesh, Salvi, Massimo, Deo, Ravinesh C., Barua, Prabal Datta, Ciaccio, Edward J., Molinari, Filippo and Acharya, RU. Rajendra |
| Journal Title | IEEE Access |
| Journal Citation | 11, pp. 108982-108994 |
| Number of Pages | 13 |
| Year | 2023 |
| Place of Publication | United States |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2023.3321273 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/10268413 |
| Abstract | One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very 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 medicine 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 very lucrative area, in terms of data inference and delivering meaningful insights. This study presents an automated method for segmenting histopathological images of human prostate glands. The main focus is developing new methods for segmenting histopathological images of prostate gland using a multi-channel algorithm with an attention mechanism to detect 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 propose for semantic segmentation of prostate glands the potential to provide accurate segmentation versus tumor features, which has significant implications for medical screening applications. |
| Keywords | Prostate cancer; image processing; histopathology images; digital image analysis; computational pathology; artificial intelligence |
| ANZSRC Field of Research 2020 | 400304. Biomedical imaging |
| 420311. Health systems | |
| 460207. Modelling and simulation | |
| Byline Affiliations | Manipal Institute of Technology, India |
| Polytechnic University of Turin, Italy | |
| School of Mathematics, Physics and Computing | |
| Cogninet Australia, Australia | |
| School of Business | |
| University of Technology Sydney | |
| Columbia University, United States | |
| Kumamoto University, Japan |
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