Title: Automated detection and grading of prostate cancer in multiparametric MRI

Authors: Prashant Ramesh Kharote; Manoj S. Sankhe; Deepak Patkar

Addresses: Electronics and Telecommunication Department, MPSTME, NMIMS University, Mumbai, India ' Electronics and Telecommunication Department, MPSTME, NMIMS University, Mumbai, India ' Imaging Section, Nanavati Super Specialty Hospital, Mumbai, India

Abstract: Prostate cancer is a major health issue worldwide and automatic segmentation of prostate from magnetic resonance imaging (MRI) is crucial task in image guided intervention. The objective of this paper is to develop a transparent and meticulous feature learning framework for prostate cancer detection and grading of prostate cancer using multiparametric magnetic resonance imaging (MPMRI). Prostate cancer is confirmed using approved rules of prostate cancer diagnosis from MPMRI data. The clustering is done in apparent diffusion coefficient (ADC) and diffusion weighted images (DWI) to obtain a probabilistic map which confirms cancerous region. The performance of proposed work is enormously tested on the dataset that contains T2Weighted, DWI and ADC map images of 236 subjects. In this study a total of 218 regions were used for analysis which includes 53 non-cancerous regions and 165 cancerous lesions. We have obtained tumour detection accuracy of 93.2% and AUC of 0.94 by using random forest classifier. The results yield by proposed algorithm is validated by two experienced radiologists.

Keywords: prostate; segmentation; deformable model; multiparametric magnetic resonance imaging; MPMRI; atlas-based segmentation; active contour model; deep learning; PIRADS; prostate cancer; classifier.

DOI: 10.1504/IJBET.2022.128088

International Journal of Biomedical Engineering and Technology, 2022 Vol.40 No.4, pp.372 - 395

Received: 12 May 2020
Accepted: 23 Aug 2020

Published online: 05 Jan 2023 *

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