픽셀 단위 컨볼루션 네트워크를 이용한 복부 컴퓨터 단층촬영 영상 기반 골전이암 병변 검출 알고리즘 개발 (Development of Bone Metastasis Detection Algorithm on Abdominal Computed Tomography Image using Pixel Wise Fully Convolutional Network)

서지정보

· 발행기관 : 대한의용생체공학회
· 수록지 정보 : 의공학회지 / 38권 / 6호 / 321 ~ 329페이지
· 저자명 : 김주영, 이시영, 김규리, 조경원, 유승민, 소순원, 박은경, 조백환, 최동일, 박훈기, 김인영

초록

This paper presents a bone metastasis Detection algorithm on abdominal computed tomography images for early detection using fully convolutional neural networks. The images were taken from patients with various cancers (such as lung cancer, breast cancer, colorectal cancer, etc), and thus the locations of those lesions were varied.
To overcome the lack of data, we augmented the data by adjusting the brightness of the images or flipping the images.
Before the augmentation, when 70% of the whole data were used in the pre-test, we could obtain the pixel-wise sensitivity of 18.75%, the specificity of 99.97% on the average of test dataset. With the augmentation, we could obtain the sensitivity of 30.65%, the specificity of 99.96%. The increase in sensitivity shows that the augmentation was effective. In the result obtained by using the whole data, the sensitivity of 38.62%, the specificity of 99.94% and the accuracy of 99.81% in the pixel-wise. lesion-wise sensitivity is 88.89% while the false alarm per case is 0.5. The results of this study did not reach the level that could substitute for the clinician. However, it may be helpful for radiologists when it can be used as a screening tool.

영어초록

This paper presents a bone metastasis Detection algorithm on abdominal computed tomography images for early detection using fully convolutional neural networks. The images were taken from patients with various cancers (such as lung cancer, breast cancer, colorectal cancer, etc), and thus the locations of those lesions were varied.
To overcome the lack of data, we augmented the data by adjusting the brightness of the images or flipping the images.
Before the augmentation, when 70% of the whole data were used in the pre-test, we could obtain the pixel-wise sensitivity of 18.75%, the specificity of 99.97% on the average of test dataset. With the augmentation, we could obtain the sensitivity of 30.65%, the specificity of 99.96%. The increase in sensitivity shows that the augmentation was effective. In the result obtained by using the whole data, the sensitivity of 38.62%, the specificity of 99.94% and the accuracy of 99.81% in the pixel-wise. lesion-wise sensitivity is 88.89% while the false alarm per case is 0.5. The results of this study did not reach the level that could substitute for the clinician. However, it may be helpful for radiologists when it can be used as a screening tool.

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