게이트심장혈액풀검사에서 딥러닝 기반 좌심실 영역 분할방법의 유용성 평가

• 영문명: Evaluating Usefulness of Deep Learning Based Left Ventricle Segmentation in Cardiac Gated Blood Pool Scan
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 오주영(Joo-Young Oh) 정의환(Eui-Hwan Jeong) 이주영(Joo-Young Lee) 박훈희(Hoon-Hee Park)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제45권 제2호, 151~158쪽, 전체 8쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2022.04.30

영문 초록

The Cardiac Gated Blood Pool (GBP) scintigram, a nuclear medicine imaging, calculates the left ventricular Ejection Fraction (EF) by segmenting the left ventricle from the heart. However, in order to accurately segment the substructure of the heart, specialized knowledge of cardiac anatomy is required, and depending on the expert s processing, there may be a problem in which the left ventricular EF is calculated differently. In this study, using the DeepLabV3 architecture, GBP images were trained on 93 training data with a ResNet-50 backbone. Afterwards, the trained model was applied to 23 separate test sets of GBP to evaluate the reproducibility of the region of interest and left ventricular EF. Pixel accuracy, dice coefficient, and IoU for the region of interest were 99.32±0.20, 94.65±1.45, 89.89±2.62(%) at the diastolic phase, and 99.26±0.34, 90.16±4.19, and 82.33±6.69(%) at the systolic phase, respectively. Left ventricular EF was calculated to be an average of 60.37±7.32% in the ROI set by humans and 58.68±7.22% in the ROI set by the deep learning segmentation model. (p<0.05) The automated segmentation method using deep learning presented in this study similarly predicts the average human-set ROI and left ventricular EF when a random GBP image is an input. If the automatic segmentation method is developed and applied to the functional examination method that needs to set ROI in the field of cardiac scintigram in nuclear medicine in the future, it is expected to greatly contribute to improving the efficiency and accuracy of processing and analysis by nuclear medicine specialists.

목차

Ⅰ. 서 론

Ⅱ. 대상 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

게이트심장혈액풀검사 심박출계수 딥러닝 관심영역 의미론적 분할 Gated blood pool scan Ejection Fraction Deep learning ROI Semantic segmentation