Cascaded architecture for segmenting prostate cancer lesions in biparametric MRI

Name: Chaloemphon Thipkasorn

Organization : Prince of Songkla University. College of Digital Science

Name: Sitthichok Chaichulee

Organization : Prince of Songkla University. Faculty of Medicine

Name: Tanan Bejrananda

Organization : Prince of Songkla University. Faculty of Medicine

Name: Teeravut Tubtawee

Organization : Prince of Songkla University. Faculty of Medicine

LCSH: Prostate -- Cancer -- Alternative treatment -- Research

LCSH: Image segmentation

LCSH: Magnetic resonance imaging

Abstract: Prostate cancer (PCa) is one of the most common cancers in men. Early detection and appropriate treatment are crucial for managing the disease. Clinical approaches can assess prostate cancer status based on the PSA level in the blood. However, it is difficult t o d etermine t he e xact location of the cancerous lesions in the prostate. Physicians typically use biparametric magnetic resonance imaging (bpMRI) to identify suspicious areas, but its complexity and variability make in- terpretation challenging. This study investigated a convolutional neural network (CNN) approach for for segmentation of prostate cancer lesions in bpMRI. We used a cascaded two-stage approach with two SegResNet models. The first m odel h elps t o narrow down the area for segmenting cancerous lesions. The second model segments cancerous lesions within the prostate area. The model processes 3D bpMRI images with T2W, DWI and ADC sequences. The model was trained on the PI-CAI 2022 dataset. The performance of the model was evaluated externally using the Prostate-158 and our PSU-mpMRI datasets. Our model showed higher performance compared to a single SegResNet baseline. We achieved a Dice score of 0.5334, 0.4221 and 0.3588 for the PI-CAI 2022, Prostate-158 and PSU-mpMRI datasets, respectively. Our model demonstrated generalisability towards external data.

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2024

Modified: 2025-05-22

Issued: 2025-05-22

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BibliograpyCitation : In IEEE Thailand Section (IEEE Computer Society Thailand Chapter) and Prince of Songkla University. College of Computing. The 21st International Joint Conference on Computer Science and Software Engineering (JCSSE 2024)) (pp.167-173). Phuket : Prince of Songkla University

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