http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
흉부 CT 영상에서 심층 감독 및 하이브리드 병변 초점 손실 함수를 활용한 폐암 분할 개선
이민진(Min Jin Lee),오윤선(Yoon-Seon Oh),홍헬렌(Helen Hong) 한국컴퓨터그래픽스학회 2024 컴퓨터그래픽스학회논문지 Vol.30 No.1
Lung cancer segmentation in chest CT images is challenging due to the varying sizes of tumors and the presence of surrounding structures with similar intensity values. To address these issues, we propose a lung cancer segmentation network that incorporates deep supervision and utilizes UNet3+ as the backbone. Additionally, we propose a hybrid lesion focal loss function comprising three components: pixel-based, region-based, and shape-based, which allows us to focus on the smaller tumor regions relative to the background and consider shape information for handling ambiguous boundaries. We validate our proposed method through comparative experiments with UNet and UNet3+ and demonstrate that our proposed method achieves superior performance in terms of Dice Similarity Coefficient (DSC) for tumors of all sizes.