LLDSNet: Limited Labeled Data Segmentation Network for COVID-19 CT lesion segmentation

Jingyao Liu; Feng Qu; Weili Shi; Chen Yang; Zhengang Jiang

doi:10.1117/12.2668266

20 April 2023 LLDSNet: Limited Labeled Data Segmentation Network for COVID-19 CT lesion segmentation

Jingyao Liu, Feng Qu, Weili Shi, Chen Yang, Zhengang Jiang

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 1260224 (2023) https://doi.org/10.1117/12.2668266
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

With the COVID-19 outbreak in 2019, the world is facing a major crisis and people's health is at serious risk. Accurate segmentation of lesions in CT images can help doctors understand disease infections, prescribe the right medicine and control patients' conditions. Fast and accurate diagnosis not only can make the limited medical resources get reasonable allocation, but also can control the spread of disease, and computer-aided diagnosis can achieve this purpose, so this paper proposes a deep learning segmentation network LLDSNet based on a small amount of data, which is divided into two modules: contextual feature-aware module (CFAM) and shape edge detection module (SEDM). Due to the different morphology of lesions in different CT, lesions with dispersion, small lesion area and background area imbalance, lesion area and normal area boundary blurred, etc. The problem of lesion segmentation in COVID-19 poses a major challenge. The CFAM can effectively extract the overall and local features, and the SEDM can accurately find the edges of the lesion area to segment the lesions in this area. The hybrid loss function is used to avoid the class imbalance problem and improve the overall network performance. It is demonstrated that LLDSNet dice achieves 0.696 for a small number of data sets, and the best performance compared to five currently popular segmentation networks.

Citation Download Citation

Jingyao Liu, Feng Qu, Weili Shi, Chen Yang, and Zhengang Jiang "LLDSNet: Limited Labeled Data Segmentation Network for COVID-19 CT lesion segmentation", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 1260224 (20 April 2023); https://doi.org/10.1117/12.2668266

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KEYWORDS

Image segmentation

Feature extraction

COVID 19

Edge detection

Computed tomography

Convolution

Education and training

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