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http://dx.doi.org/10.9717/kmms.2020.23.5.625

A Study on Lung Cancer Segmentation Algorithm using Weighted Integration Loss on Volumetric Chest CT Image  

Jeong, Jin Gyo (Dept. of Biomedical Engineering, Gachon University)
Kim, Young Jae (Dept. of Biomedical Engineering, Gachon University)
Kim, Kwang Gi (Dept. of Biomedical Engineering, Gachon University)
Publication Information
Journal of Korea Multimedia Society / v.23, no.5, 2020 , pp. 625-632 More about this Journal
Abstract
In the diagnosis of lung cancer, the tumor size is measured by the longest diameter of the tumor in the entire slice of the CT. In order to accurately estimate the size of the tumor, it is better to measure the volume, but there are some limitations in calculating the volume in the clinic. In this study, we propose an algorithm to segment lung cancer by applying a custom loss function that combines focal loss and dice loss to a U-Net model that shows high performance in segmentation problems in chest CT images. The combination of values of the various parameters in custom loss function was compared to the results of the model learned. The purposed loss function showed F1 score of 88.77%, precision of 87.31%, recall of 90.30% and average precision of 0.827 at α=0.25, γ=4, β=0.7. The performance of the proposed custom loss function showed good performance in lung cancer segmentation.
Keywords
Lung Cancer; Segmentation; U-Net; Custom Loss Function; Computed Tomography;
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Times Cited By KSCI : 2  (Citation Analysis)
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