A lightweight neural network with multiscale feature enhancement for liver CT segmentation
Date
2022-12-01Author
Ansari, Mohammed YusufYang, Yin
Balakrishnan, Shidin
Abinahed, Julien
Al-Ansari, Abdulla
Warfa, Mohamed
Almokdad, Omran
Barah, Ali
Omer, Ahmed
Singh, Ajay Vikram
Meher, Pramod Kumar
Bhadra, Jolly
Halabi, Osama
Azampour, Mohammad Farid
Navab, Nassir
Wendler, Thomas
Dakua, Sarada Prasad
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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.
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