흉부 볼륨 CT영상에서 Weighted Integration Loss을 이용한 폐암 분할 알고리즘 연구A Study on Lung Cancer Segmentation Algorithm using Weighted Integration Loss on Volumetric Chest CT Image
- Other Titles
- A Study on Lung Cancer Segmentation Algorithm using Weighted Integration Loss on Volumetric Chest CT Image
- Authors
- 정진교; 김영재; 김광기
- Issue Date
- May-2020
- Publisher
- 한국멀티미디어학회
- Keywords
- Lung Cancer; Segmentation; U-Net; Custom Loss Function; Computed Tomography
- Citation
- 멀티미디어학회논문지, v.23, no.5, pp.625 - 632
- Journal Title
- 멀티미디어학회논문지
- Volume
- 23
- Number
- 5
- Start Page
- 625
- End Page
- 632
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/47125
- ISSN
- 1229-7771
- 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 . The performance of the proposed custom loss function showed good performance in lung cancer segmentation.
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