ResQu-Net: Effective prostate’s peripheral zone segmentation leveraging the representational power of attention-based mechanisms

Zaridis, Dimitrios; Mylona, Eugenia; Tachos, Nikolaos; Kalantzopoulos, Charalampos; Marias, Kostas; Tsiknakis, Manolis; Matsopoulos, George; Koutsouris, Dimitrios; Fotiadis, Dimitrios

dc.contributor.author	Zaridis, Dimitrios
dc.contributor.author	Mylona, Eugenia
dc.contributor.author	Tachos, Nikolaos
dc.contributor.author	Kalantzopoulos, Charalampos
dc.contributor.author	Marias, Kostas
dc.contributor.author	Tsiknakis, Manolis
dc.contributor.author	Matsopoulos, George
dc.contributor.author	Koutsouris, Dimitrios
dc.contributor.author	Fotiadis, Dimitrios
dc.date.accessioned	2026-01-22T10:00:02Z
dc.date.available	2026-01-22T10:00:02Z
dc.identifier.issn	1746-8108	el
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/63243
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.30938
dc.rights	Αναφορά Δημιουργού 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/gr/	*
dc.subject	Deep learning	en
dc.title	ResQu-Net: Effective prostate’s peripheral zone segmentation leveraging the representational power of attention-based mechanisms	en
heal.type	journalArticle
heal.classification	Deep Learning	en
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-03-06
heal.bibliographicCitation	Zaridis, D. I., Mylona, E., Tachos, N., Kalantzopoulos, C. Ν., Marias, K., Tsiknakis, M., Matsopoulos, G. K., Koutsouris, D. D., & Fotiadis, D. I. (2024). ResQu-Net: Effective prostate’s peripheral zone segmentation leveraging the representational power of attention-based mechanisms. Biomedical Signal Processing and Control, 93, 106187. https://doi.org/10.1016/j.bspc.2024.106187	en
heal.abstract	Prostate cancer is a leading cause of male cancer worldwide. With more than 70 % of prostate cancers arising in the peripheral zone of the prostate, accurate segmentation of this region is of paramount importance for the effective diagnosis and treatment of the disease. Although peripheral zone is well recognized as one of the most challenging regions to delineate within the prostate, no algorithms specifically tailored for this segmentation task are currently available. The present study introduces a new deep learning (DL) algorithm, named as ResQu-Net, which is designed to accurately segment the peripheral zone (PZ) of the prostate on T2-weighted magnetic resonance imaging (MRI). Using three publicly available datasets, the ResQu-Net outperformed the six DL segmentation models used for comparison, namely the Attention U-Net, the Dense2U-Net, the Proper-Net, the TransU-net, the U-Net, and the USE-Net, demonstrating superior performance for different anatomical regions, such as the apex, the midgland and the base. The assessment of the suggested approach was conducted not only quantitatively (Sensitivity, Balanced Accuracy, Dice Score, 95 % Hausdorff Distance, and Average Surface Distance) but also qualitatively. For the qualitative evaluation the feature maps obtained from the last layers of each model were compared with the Density Map of the Ground Truth annotations using root mean squared error. Overall, the ResQu-Net model exhibits improved performance compared to other models, of more than 5 % and 1.87 mm in terms of Dice Score and 95 % Hausdorff Distance, respectively. These advancements may contribute significantly in addressing the challenges associated with PZ segmentation, and ultimately enabling improved clinical decision-making and patient outcomes.	en
heal.sponsor	This work is supported by the ProCancer-I project, funded by the European Union’s Horizon 2020 research and innovation program under grant agreement No 952159 and reflects only the authors’ view. The Commission is not responsible for any use that may be made of the information it contains.	en
heal.publisher	Elsevier	en
heal.journalName	Biomedical Signal Processing and Control	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false
dc.identifier.doi	https://doi.org/10.1016/j.bspc.2024.106187	el