A Lightweight 3D Segmentation Network for Abdominal Liver in CT Image
Pages 40 - 45
Abstract
Abnormal liver function is linked to a variety of disorders. Precise and quick automatic liver segmentation can help clinicians make better diagnosis and treatment decisions. With the development of computer vision and deep learning approaches, there are more solutions for biomedical image segmentation tasks. In recent years, the U-Net architecture is by far the most widely used backbone architecture for biomedical image segmentation. Deep convolutional neural networks-based semantic segmentation has achieved sufficient accuracy. However, the scale of high-precision networks is growing, requiring an increasing amount of storage and computational resources. Furthermore, the deep neural network's operating time is lengthy, making it difficult to satisfy practical needs. As a result, the lightweight convolutional neural network design is used to the semantic segmentation task. As a consequence, in this article, a lightweight convolutional neural network is proposed to solve the aforementioned problems in the task of biomedical image segmentation. 3D U-Net is used as the backbone architecture and a modification of the Ghost module from GhostNet is introduced to boost up the effectiveness and the learning efficiency. The experimental results demonstrate that the proposed network improved the segmentation performance with fewer network parameters and requiring less floating-point computation.
References
[1]
Hyuna Sung, Jacques Ferlay, Rebecca L Siegel, Mathieu Laversanne, Isabelle Soerjomataram, Ahmedin Jemal, and Freddie Bray. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians, 71(3):209–249, 2021.
[2]
Jonathan Long, Evan Shelhamer, and Trevor Darrell. Fully convolutional networksfor semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3431–3440, 2015.
[3]
Robert Korez, Boštjan Likar, Franjo Pernuš, and Tomaž Vrtovec. Model-based segmentation of vertebral bodies from mr images with 3d cnns. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 433–441. Springer, 2016.
[4]
Pim Moeskops, Jelmer M Wolterink, Bas HM van der Velden, Kenneth GAGilhuijs, Tim Leiner, Max A Viergever, and Ivana Išgum. Deep learning for multitask medical image segm entation in multiple modalities. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 478–486. Springer, 2016.
[5]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 234–241. Springer, 2015.
[6]
Özgün Çiçek, Ahmed Abdulkadir, Soeren S Lienkamp, Thomas Brox, and OlafRonneberger. 3d u-net: learning dense volumetric segmentation from sparse annotation. In International Conference on Medical Image Computing and ComputerAssisted Intervention, pages 424–432. Springer, 2016.
[7]
Fausto Milletari, Nassir Navab, and Seyed-Ahmad Ahmadi. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In 2016 Fourth International Conference on 3D Vision (3DV), pages 565–571. IEEE, 2016.
[8]
Arjun Rao, Philipp Plank, Andreas Wild, and Wolfgang Maass. A long shortterm memory for ai applications in spike-based neuromorphic hardware. Nature Machine Intelligence, 4(5):467–479, 2022.
[9]
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William JDally, and Kurt Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and < 0.5 mb model size. arXiv preprint arXiv:1602.07360, 2016.
[10]
Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin,Sicheng Zhao, and Kurt Keutzer. Squeezenext: Hardware-aware neural network design. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pages 1638–1647, 2018.
[11]
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, and Jian Sun. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6848– 6856, 2018.
[12]
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. Shufflenet v2: Practical guidelines for efficient cnn architecture design. In Proceedings of the European Conference on Computer Vision (ECCV), pages 116–131, 2018.
[13]
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang,Tobias Weyand, Marco Andreetto, and Hartwig Adam. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861, 2017.
[14]
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and LiangChieh Chen. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4510– 4520, 2018.
[15]
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, MingxingTan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Searching for mobilenetv3. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 1314–1324, 2019.
[16]
Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu.Ghostnet: More features from cheap operations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1580–1589, 2020.
[17]
François Chollet. Xception: Deep learning with depthwise separable convolutions.
[18]
In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1251–1258, 2017.
[19]
Nazanin Beheshti and Lennart Johnsson. Squeeze u-net: A memory and energyefficient image segmentation network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pages 364–365, 2020.
[20]
Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12):2481–2495, 2017.
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- A Lightweight 3D Segmentation Network for Abdominal Liver in CT Image
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Published In
November 2022
202 pages
ISBN:9781450397155
DOI:10.1145/3577117
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Published: 25 February 2023
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ICAIP 2022
ICAIP 2022: 2022 6th International Conference on Advances in Image Processing
November 18 - 20, 2022
Zhanjiang, China
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