복부 CT 영상에서 밝기값 정규화 및 Faster R-CNN을 이용한 자동 췌장 검출 (Automatic Pancreas Detection on Abdominal CT Images using Intensity Normalization and Faster R-CNN)

서지정보

· 발행기관 : 한국멀티미디어학회
· 수록지 정보 : 멀티미디어학회논문지 / 24권 / 3호 / 396 ~ 405페이지
· 저자명 : 최시은, 이성은, 홍헬렌

초록

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient’s pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model’s performance on each data. The results show that the top data’s mAP@.50IoU achieved 91.7% and the bottom data’s mAP@.50IoU achieved 95.4%, and the highest performance was the middle data’s mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

영어초록

In surgery to remove pancreatic cancer, it is important to figure out the shape of a patient’s pancreas. However, previous studies have a limit to detect a pancreas automatically in abdominal CT images, because the pancreas varies in shape, size and location by patient. Therefore, in this paper, we propose a method of learning various shapes of pancreas according to the patients and adjacent slices using Faster R-CNN based on Inception V2, and automatically detecting the pancreas from abdominal CT images. Model training and testing were performed using the NIH Pancreas-CT Dataset, and intensity normalization was applied to all data to improve pancreatic detection accuracy. Additionally, according to the shape of the pancreas, the test dataset was classified into top, middle, and bottom slices to evaluate the model’s performance on each data. The results show that the top data’s mAP@.50IoU achieved 91.7% and the bottom data’s mAP@.50IoU achieved 95.4%, and the highest performance was the middle data’s mAP@.50IoU, 98.5%. Thus, we have confirmed that the model can accurately detect the pancreas in CT images.

참고자료

· 없음