2 research outputs found
Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures
Prostate cancer is the second-most frequently diagnosed cancer and the sixth
leading cause of cancer death in males worldwide. The main problem that
specialists face during the diagnosis of prostate cancer is the localization of
Regions of Interest (ROI) containing a tumor tissue. Currently, the
segmentation of this ROI in most cases is carried out manually by expert
doctors, but the procedure is plagued with low detection rates (of about
27-44%) or overdiagnosis in some patients. Therefore, several research works
have tackled the challenge of automatically segmenting and extracting features
of the ROI from magnetic resonance images, as this process can greatly
facilitate many diagnostic and therapeutic applications. However, the lack of
clear prostate boundaries, the heterogeneity inherent to the prostate tissue,
and the variety of prostate shapes makes this process very difficult to
automate.In this work, six deep learning models were trained and analyzed with
a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and
Universitat Politecnica de Catalunya. We carried out a comparison of multiple
deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention
Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy
loss function. The analysis was performed using three metrics commonly used for
image segmentation: Dice score, Jaccard index, and mean squared error. The
model that give us the best result segmenting all the zones was R2U-Net, which
achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error,
respectively
FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation
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.Comment: This paper has been accepted at the 22nd Mexican International
Conference on Artificial Intelligence (MICAI 2023