A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images
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 p...
| Published in: | IEEE Access |
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| Main Authors: | , , , , , , , , , |
| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10268413/ |
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| author | 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 |
| author_facet | 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 |
| author_sort | Mahesh Anil Inamdar |
| collection | DOAJ |
| container_title | IEEE Access |
| description | 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. |
| format | Article |
| id | doaj-art-db916f7b00a94123938ed171bfc3d4d8 |
| institution | Directory of Open Access Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| spelling | doaj-art-db916f7b00a94123938ed171bfc3d4d82025-08-19T22:54:21ZengIEEEIEEE Access2169-35362023-01-011110898210899410.1109/ACCESS.2023.332127310268413A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological ImagesMahesh Anil Inamdar0U. Raghavendra1https://orcid.org/0000-0002-1124-089XAnjan Gudigar2https://orcid.org/0000-0001-5634-9103Sarvesh Bhandary3Massimo Salvi4https://orcid.org/0000-0001-7225-7401Ravinesh C. Deo5https://orcid.org/0000-0002-2290-6749Prabal Datta Barua6https://orcid.org/0000-0001-5117-8333Edward J. Ciaccio7Filippo Molinari8https://orcid.org/0000-0003-1150-2244U. Rajendra Acharya9https://orcid.org/0000-0003-2689-8552Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Electronics and Telecommunications, Biolab, PolitoBIOMed Laboratory, Politecnico di Torino, Turin, ItalySchool of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, AustraliaCogninet Brain Team, Cogninet Australia, Sydney, NSW, AustraliaDepartment of Medicine, Columbia University, New York, NY, USADepartment of Electronics and Telecommunications, Biolab, PolitoBIOMed Laboratory, Politecnico di Torino, Turin, ItalySchool of Mathematics, Physics, and Computing, University of Southern Queensland, Springfield, QLD, AustraliaOne 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.https://ieeexplore.ieee.org/document/10268413/Prostate cancerimage processinghistopathology imagesdigital image analysiscomputational pathologyartificial intelligence |
| spellingShingle | 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 A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images Prostate cancer image processing histopathology images digital image analysis computational pathology artificial intelligence |
| title | A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images |
| title_full | A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images |
| title_fullStr | A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images |
| title_full_unstemmed | A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images |
| title_short | A Novel Attention-Based Model for Semantic Segmentation of Prostate Glands Using Histopathological Images |
| title_sort | novel attention based model for semantic segmentation of prostate glands using histopathological images |
| topic | Prostate cancer image processing histopathology images digital image analysis computational pathology artificial intelligence |
| url | https://ieeexplore.ieee.org/document/10268413/ |
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