A lightweight neural network with multiscale feature enhancement for liver CT segmentation
Author | Ansari, Mohammed Yusuf |
Author | Yang, Yin |
Author | Balakrishnan, Shidin |
Author | Abinahed, Julien |
Author | Al-Ansari, Abdulla |
Author | Warfa, Mohamed |
Author | Almokdad, Omran |
Author | Barah, Ali |
Author | Omer, Ahmed |
Author | Singh, Ajay Vikram |
Author | Meher, Pramod Kumar |
Author | Bhadra, Jolly |
Author | Halabi, Osama |
Author | Azampour, Mohammad Farid |
Author | Navab, Nassir |
Author | Wendler, Thomas |
Author | Dakua, Sarada Prasad |
Available date | 2023-01-11T06:37:51Z |
Publication Date | 2022-12-01 |
Publication Name | Scientific Reports |
Identifier | http://dx.doi.org/10.1038/s41598-022-16828-6 |
Citation | Ansari, M.Y., Yang, Y., Balakrishnan, S. et al. A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Sci Rep 12, 14153 (2022). https://doi.org/10.1038/s41598-022-16828-6 |
Abstract | Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million. |
Sponsor | This publication was made possible by NPRP-11S-1219-170106 from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work, and are solely the responsibility of the authors. |
Language | en |
Publisher | Nature Research |
Subject | A lightweight neural network liver CT segmentation |
Type | Article |
Issue Number | 1 |
Volume Number | 12 |
ESSN | 2045-2322 |
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Research of Qatar University Young Scientists Center [205 items ]