Version 1
: Received: 23 August 2024 / Approved: 25 August 2024 / Online: 26 August 2024 (11:59:28 CEST)
How to cite:
Arora, R.; Kumar, A.; Soni, A. Deep Learning Approaches for Enhanced Kidney Segmentation: Evaluating U-Net and Attention U-Net with Cross-Entropy and Focal Loss Functions. Preprints2024, 2024081816. https://doi.org/10.20944/preprints202408.1816.v1
Arora, R.; Kumar, A.; Soni, A. Deep Learning Approaches for Enhanced Kidney Segmentation: Evaluating U-Net and Attention U-Net with Cross-Entropy and Focal Loss Functions. Preprints 2024, 2024081816. https://doi.org/10.20944/preprints202408.1816.v1
Arora, R.; Kumar, A.; Soni, A. Deep Learning Approaches for Enhanced Kidney Segmentation: Evaluating U-Net and Attention U-Net with Cross-Entropy and Focal Loss Functions. Preprints2024, 2024081816. https://doi.org/10.20944/preprints202408.1816.v1
APA Style
Arora, R., Kumar, A., & Soni, A. (2024). Deep Learning Approaches for Enhanced Kidney Segmentation: Evaluating U-Net and Attention U-Net with Cross-Entropy and Focal Loss Functions. Preprints. https://doi.org/10.20944/preprints202408.1816.v1
Chicago/Turabian Style
Arora, R., Anoop Kumar and Arpita Soni. 2024 "Deep Learning Approaches for Enhanced Kidney Segmentation: Evaluating U-Net and Attention U-Net with Cross-Entropy and Focal Loss Functions" Preprints. https://doi.org/10.20944/preprints202408.1816.v1
Abstract
Abstract— The rising occurrence of chronic kidney disease represents a significant health concern, impacting over 10% of individuals. Magnetic Resonance Imaging (MRI) biomarkers identify pathological alterations, are non-invasive, and can help lessen biopsy requirements and complications in chronic kidney disease patients. Total volume is the most assessed metric in autosomal dominant polycystic kidney disease patients, assisting in tracking chronic kidney disease progression. Kidney segmentation is essential for evaluating renal volume. Nevertheless, it often relies on manual segmentation, which is time-consuming and highly subjective. However, Deep Learning (DL) techniques have led to creating algorithms that provide accurate, cost-effective, and user-independent outcomes. Therefore, this study explores DL-based strategies for kidney segmentation, particularly U-Net and Attention U-Net. Both architectures were assessed with the standard cross-entropy loss function and a focal cross-entropy loss function. Ultimately, the proposed methods were used for both 2-class and 3-class segmentation to improve the segmentation of border regions. The highest Dice coefficient achieved on the testing set was 0.966.
Keywords
Index Terms— Deep Learning; Kidney; Magnetic Resonance Imaging; U-Net
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
