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CC BY 4.0 · Indian J Med Paediatr Oncol
DOI: 10.1055/s-0045-1814382
Original Article

A Deep Neural Network-Based Computer-Assisted Prostate Segmentation in Biparametric Magnetic Resonance Images

Authors

  • Prashant Kharote

    1   Department of Electronics and Telecommunication Engineering, Mukesh Patel School of Technology Management and Engineering, Narsee Monjee Institute of Management Studies University, Mumbai, Maharashtra, India

  • Manoj Sankhe

    1   Department of Electronics and Telecommunication Engineering, Mukesh Patel School of Technology Management and Engineering, Narsee Monjee Institute of Management Studies University, Mumbai, Maharashtra, India

  • Deepak Patkar

    2   Department of Medical Service and Head Imaging, Nanavati Max Super Speciality Hospital, Mumbai, Maharashtra, India

  • Nikhil Bardeskar

    3   Nanavati Max Institute of Cancer Care, Nanavati Max Super Speciality Hospital, Mumbai, Maharashtra, India

Funding None.
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Abstract

Introduction

Prostate cancer is one of the most well-known cancers in men. To decrease the mortality rate associated with prostate cancer, early identification is very essential for further treatment planning. Accurate diagnosis of the cancer stage is essential for effective treatment planning.

Objectives

We propose a computer-assisted detection and diagnosis system that uses prostate segmentation along with detection and prediction of prostate cancer grades utilizing biparametric magnetic resonance images.

Materials and Methods

The study proposed included 236 patients who underwent biparametric magnetic resonance imaging scans. These scans generated T2-weighted images and DW images of 183 patients with cancer and 53 patients without cancer. The Prostate Imaging Reporting and Data System score ranged from 1 to 5. We initially generated a prostate probabilistic map using a two-way approach, and we employed a rule-based algorithm to identify the clinically significant region within the segmented prostate. The classifiers were used to forecast grades once the clinically significant issues were confirmed.

Results

The proposed system achieved a Dice similarity coefficient of 89.4% and a Hausdorff distance of 7.78 mm. The area under the receiver operating characteristic curve (AUC) is used as an indicator of the classifier's performance, with a value of 0.91, and the accuracy of the combined modality was 90.48%.

Conclusion

The stacked autoencoder is used to overcome challenges such as blurred prostate boundaries, variations in the size and shape of the prostate among subjects by extracting hidden features. The combined modality achieved higher classification accuracy and AUC compared with each individual modality. The random forest classifier demonstrated reasonably enhanced performance compared with K-nearest neighbor (k-NN) and support vector machine classifiers.

Keywords

prostate cancer - prostate segmentation - random forest - deep learning - PIRADS

Authors' Contributions

The manuscript has been read and approved by all authors and each author believes that the manuscript represents honest and genuine work. All requirements for authorship have been met while drafting this manuscript.




Publication History

Article published online:
15 December 2025

© 2025. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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