Version 1
: Received: 2 July 2024 / Approved: 2 July 2024 / Online: 3 July 2024 (11:48:03 CEST)
How to cite:
Kurucz, L.; Natali, T.; Fusaglia, M.; Dashtbozorg, B. Advances in Deep-Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging. Preprints2024, 2024070244. https://doi.org/10.20944/preprints202407.0244.v1
Kurucz, L.; Natali, T.; Fusaglia, M.; Dashtbozorg, B. Advances in Deep-Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging. Preprints 2024, 2024070244. https://doi.org/10.20944/preprints202407.0244.v1
Kurucz, L.; Natali, T.; Fusaglia, M.; Dashtbozorg, B. Advances in Deep-Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging. Preprints2024, 2024070244. https://doi.org/10.20944/preprints202407.0244.v1
APA Style
Kurucz, L., Natali, T., Fusaglia, M., & Dashtbozorg, B. (2024). Advances in Deep-Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging. Preprints. https://doi.org/10.20944/preprints202407.0244.v1
Chicago/Turabian Style
Kurucz, L., Matteo Fusaglia and Behdad Dashtbozorg. 2024 "Advances in Deep-Learning Methods for Prostate Segmentation and Volume Estimation in Ultrasound Imaging" Preprints. https://doi.org/10.20944/preprints202407.0244.v1
Abstract
Accurate prostate volume estimation is crucial for effective prostate disease management. Ultrasound (US) imaging, particularly transrectal ultrasound, offers a cost-effective and rapid assessment. However, US images often suffer from artifacts and poor contrast, making prostate volume estimation challenging. This review explores recent advancements in deep learning (DL) techniques for automatic prostate segmentation in US images as a primary step for prostate volume estimation. We examine various DL architectures, including traditional U-Net modifications and innovative designs incorporating residual connections, multi-directional image data, and attention mechanisms. Additionally, we discuss pre-processing methods to enhance image quality, the integration of shape information, and strategies to improve the consistency and robustness of DL models. The effectiveness of these techniques is evaluated through metrics such as Dice Similarity Coefficient, Jaccard Index, and Hausdorff Distance. The review highlights the potential of DL in improving prostate volume estimation accuracy and reducing clinical workload, while also identifying areas for future research to enhance model performance and generalizability.
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.
