한국인터넷정보학회 논문지
Current Result Document :
| 한글제목(Korean Title) |
A New Hyper Parameter of Hounsfield Unit Range in Liver Segmentation |
| 영문제목(English Title) |
A New Hyper Parameter of Hounsfield Unit Range in Liver Segmentation |
| 저자(Author) |
Kangjik Kim
Junchul Chun
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| 원문수록처(Citation) |
VOL 21 NO. 03 PP. 0103 ~ 0111 (2020. 06) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Liver cancer is the most fatal cancer that occurs worldwide. In order to diagnose liver cancer, the patient's physical condition was checked by using a CT technique using radiation. Segmentation was needed to diagnose the liver on the patient's abdominal CT scan, which the radiologists had to do manually, which caused tremendous time and human mistakes. In order to automate, researchers attempted segmentation using image segmentation algorithms in computer vision field, but it was still time-consuming because of the interactive based and the setting value. To reduce time and to get more accurate segmentation, researchers have begun to attempt to segment the liver in CT images using CNNs, which show significant performance in various computer vision fields. The pixel value, or numerical value, of the CT image is called the Hounsfield Unit (HU) value, which is a relative representation of the transmittance of radiation, and usually ranges from about -2000 to 2000. In general, deep learning researchers reduce or limit this range and use it for training to remove noise and focus on the target organ. Here, we observed that the range of HU values was limited in many studies but different in various liver segmentation studies, and assumed that performance could vary depending on the HU range. In this paper, we propose the possibility of considering HU value range as a hyper parameter. U-Net and ResUNet were used to compare and experiment with different HU range limit preprocessing of CHAOS dataset under limited conditions. As a result, it was confirmed that the results are different depending on the HU range. This proves that the range limiting the HU value itself can be a hyper parameter, which means that there are HU ranges that can provide optimal performance for various models.
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| 키워드(Keyword) |
U-Net
Liver Segmentation
Hounsfield Unit (HU)
ResUNet
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| 파일첨부 |
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