Name: FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
Keywords: Pròstata -- Càncer, FOS: Computer and information sciences, Image segmentation, Prostate cancer, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Computer Science - Computer Vision and Pattern Recognition, Deep learning, Electrical Engineering and Systems Science - Image and Video Processing

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Article , Preprint , Conference object 09 Nov 2023Embargo end date: 01 Jan 2023 English Publisher:Springer Nature Switzerland

Authors: Quihui Rubio, Pablo Cesar; Flores Araiza, Daniel; González Mendoza, Miguel; Mata Miquel, Cristian; Ochoa Ruíz, Gilberto;

doi: 10.1007/978-3-031-47640-2_14 , 10.48550/arxiv.2309.01322

arXiv: 2309.01322

handle: 2117/399260

FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

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Abstract

This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis. The proposed model is compared to seven different U-Net-based architectures. The automatic segmentation performance of each model of the central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were evaluated using Dice Score (DSC), and the Intersection over Union (IoU) metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of 76.9% in the test set, outperforming most of the studied models in this work except from R2U-Net and attention R2U-Net architectures.

This paper has been accepted at the 22nd Mexican International Conference on Artificial Intelligence (MICAI 2023)

Related Organizations

Universitat Politècnica de Catalunya
Spain
Monterrey Institute of Technology and Higher Education
Mexico
Universitat Polite`cnica de Catalunya
Spain
HOSPITAL SANT JOAN DE DÉU
Spain

Keywords

Pròstata -- Càncer, FOS: Computer and information sciences, Image segmentation, Prostate cancer, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial, Computer Science - Computer Vision and Pattern Recognition, Deep learning, Electrical Engineering and Systems Science - Image and Video Processing, U-Net, Imatges -- Segmentació, Segmentation, Àrees temàtiques de la UPC::Ciències de la salut::Medicina, FOS: Electrical engineering, electronic engineering, information engineering, Prostate -- Cancer, Attention, Intel·ligència artificial -- Aplicacions a la medicina, Uncertainty quantification, Artificial intelligence -- Medical applications, Aprenentatge profund

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