A light-weight convolutional network for liver and tumor segmentation

Mengfei Zhang; Xiaopeng Yang

doi:10.1117/12.2640121

1 August 2022 A light-weight convolutional network for liver and tumor segmentation

Mengfei Zhang, Xiaopeng Yang

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 122570B (2022) https://doi.org/10.1117/12.2640121
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

Automated segmentation of liver and lesion in contrast-enhanced abdominal computed tomography (CT) scans is a crucial step for computer-assisted diagnosis and surgical planning of liver diseases. Although deep convolutional neural networks (DCNNs) have contributed many breakthroughs in liver and lesion segmentation task from CT images, the problem remains challenging due to low segmentation accuracy and high complexity. To address these issues, we proposed a light-weight convolutional neural networks for liver and lesion segmentation from CT images, i.e., LW-Mnet. Firstly, we improve the basic module of MobileNetv3 as the backbone of LW-Mnet. The second is that we employ LWASPP to capture multi-scale feature information for enhance representation power of network. The proposed network is evaluated on the public LiTS dataset, and the results show that LW-Mnet achieved the better performance than other networks with fewer parameters and faster inference speed.

Citation Download Citation

Mengfei Zhang and Xiaopeng Yang "A light-weight convolutional network for liver and tumor segmentation", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 122570B (1 August 2022); https://doi.org/10.1117/12.2640121

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KEYWORDS

Image segmentation

Liver

Convolution

Tumors

Convolutional neural networks

Feature extraction

Performance modeling

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