갑상선 초음파 영상의 평활화 알고리즘에 따른 U-Net 기반 학습 모델 평가

• 영문명: Evaluation of U-Net Based Learning Models according to Equalization Algorithm in Thyroid Ultrasound Imaging
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 정무진(Moo-Jin Jeong) 오주영(Joo-Young Oh) 박훈희(Hoon-Hee Park) 이주영(Joo-Young Lee)
• 간행물 정보: 『방사선기술과학』제47권 제1호, 29~37쪽, 전체 9쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2024.02.29

영문 초록

This study aims to evaluate the performance of the U-Net based learning model that may vary depending on the histogram equalization algorithm. The subject of the experiment were 17 radiology students of this college, and 1,727 data sets in which the region of interest was set in the thyroid after acquiring ultrasound image data were used. The training set consisted of 1,383 images, the validation set consisted of 172 and the test data set consisted of 172. The equalization algorithm was divided into Histogram Equalization(HE) and Contrast Limited Adaptive Histogram Equalization(CLAHE), and according to the clip limit, it was divided into CLAHE8-1, CLAHE8-2. CLAHE8-3. Deep Learning was learned through size control, histogram equalization, Z-score normalization, and data augmentation. As a result of the experiment, the Attention U-Net showed the highest performance from CLAHE8-2 to 0.8355, and the U-Net and BSU-Net showed the highest performance from CLAHE8-3 to 0.8303 and 0.8277. In the case of mIoU, the Attention U-Net was 0.7175 in CLAHE8-2, the U-Net was 0.7098 and the BSU-Net was 0.7060 in CLAHE8-3. This study attempted to confirm the effects of U-Net, Attention U-Net, and BSU-Net models when histogram equalization is performed on ultrasound images. The increase in Clip Limit can be expected to increase the ROI match with the prediction mask by clarifying the boundaries, which affects the improvement of the contrast of the thyroid area in deep learning model learning, and consequently affects the performance improvement.

목차

Ⅰ. 서 론

Ⅱ. 대상 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

히스토그램 평활화 대비 제한 적응 히스토그램 평활화 초음파 의료영상 U-Net HE CLAHE Ultrasound Medical Image