자동화된 2차원 복부 CT 영상에서 딥러닝 기법을 이용한 근육 분할 기법
Automated Muscle segmentation technique using deep learning technique in 2D abdominal CT images
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초록
본 논문에서는 복부 CT 영상에서 딥러닝을 이용한 정확하고 빠른 근육 분할 기법을 제안한다. 데이터 세트는 DICOM 태그의 리스케일 슬로프, 인터셉트 정보를 이용해 픽셀 값을 정규화하고 훈련 데이터 세트에는 유사 변환을 이용해 이미지 증강을 수행했다. 증강된 훈련 데이터 세트에 U-net을 이용해 근육 영역 분할 학습을 수행했다. U-net은 생물의학 이미지 분할을 위해 고안된 fully convolutional networks 기반 모델이다. 분할된 근육 영역에서 hounsfield unit 범위로 판별된 근육 외의 부분을 제거하여 근육 마스크를 생성한다. 제안된 모델의 정확도는 98%이고, 한 장의 복부 CT 영상에서 근육을 분할하는 데 평균 1초가 소요되었다.
Abstract
In this paper, we propose an accurate and fast fully automated muscle segmentation technique using deep learning in abdominal CT images. The dataset normalized pixel values using rescale slope, intercept information in the DICOM tag, and image augmentation was performed using similarity transformation in the training dataset. We performed muscle region segmentation learning using U-net on the augmented training dataset. U-net is a fully convolutional networks based model designed for biomedical image segmentation. A muscle mask is generated by removing a portion other than the muscle determined by the hounsfield unit range from the divided muscle region. The accuracy of the proposed model was 98% and it took an average of 1 second to segment the muscles in one abdominal CT image.
참고문헌 (19)
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