2018년 컴퓨터종합학술대회
Current Result Document :
| 한글제목(Korean Title) |
적응적 U-Net을 이용한 X-레이 영상의 뼈 영역 분할 |
| 영문제목(English Title) |
Bone Area Segmentation in X-Ray images using an Adaptable U-Net |
| 저자(Author) |
호녁휜
트란딘썬
주종민
양형정
김수형
정성택
주상돈
Ngoc-Huynh Ho
Dinh-Son Tran
Jong-Min Joo
Hyung-Jeong Yang
Soo-Hyung Kim
Sung-Taek Jung
Sang-Don Joo
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| 원문수록처(Citation) |
VOL 45 NO. 01 PP. 0849 ~ 0851 (2018. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Segmentation of human bone from X-ray images has evolved as a tool for the diagnosis of bone tumor. Segmentation of a snapshot is the hardest and most complex task because there are proprietary objects and a huge variation between them using a common framework. In this paper, we have introduced a fully automatic and robust method, entitled the adaptable U-Net (aU-Net) applied in X-ray bone segmentation. This method is based on convolution neural networks (CNNs), which achieved state-of-the-art performance for automatic medical image segmentation. The experimental result show that 1) the aU-Net is more robust to segment previously unseen objects than the traditional methods, 2) the aU-Net outperforms to other conventional methods in terms of the image segmentation loss.
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| 키워드(Keyword) |
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| 파일첨부 |
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