A lightweight neural network with multiscale feature enhancement for liver CT segmentation

Ansari M, Yang Y, Balakrishnan S, Abinahed J, Al-Ansari A, Warfa M, Almokdad O, Barah A, Omer A, Singh A, Meher PK, Bhadra J, Halabi O, Azampour MF, Navab N, Wendler T, Dakua SP (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Scientific Reports Nature Publishing Group: Open Access Journals - Option B

Book Volume: 12

Article Number: 14153

Journal Issue: 1

DOI: 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.

Involved external institutions

Hamad Medical Corporation QA Qatar (QA) Hamad Bin Khalifa University QA Qatar (QA) Wake Forest University US United States (USA) (US) Bundesinstitut für Risikobewertung (BfR) DE Germany (DE) C. V. Raman Global University IN India (IN) Qatar University / جامعة قطر (Jami'at Qatar) QA Qatar (QA) Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Ansari, M., Yang, Y., Balakrishnan, S., Abinahed, J., Al-Ansari, A., Warfa, M.,... Dakua, S.P. (2022). A lightweight neural network with multiscale feature enhancement for liver CT segmentation. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-16828-6

MLA:

Ansari, Mohammedyusuf, et al. "A lightweight neural network with multiscale feature enhancement for liver CT segmentation." Scientific Reports 12.1 (2022).

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