[논문]인공지능을 활용한 초음파영상진단장치에서 초음파 팬텀 영상을 이용한 정도관리의 정량적 평가방법 연구

임연진; 황호성; 김동현; 김호철

doi:10.9718/jber.2022.43.6.390

인공지능을 활용한 초음파영상진단장치에서 초음파 팬텀 영상을 이용한 정도관리의 정량적 평가방법 연구
A Study on the Quantitative Evaluation Method of Quality Control using Ultrasound Phantom in Ultrasound Imaging System based on Artificial Intelligence 원문보기

Journal of biomedical engineering research : the official journal of the Korean Society of Medical & Biological Engineering, v.43 no.6, 2022년, pp.390 - 398

임연진 (한국의학연구소) , 황호성 (을지대학교 일반대학원 의료인공지능학과) , 김동현 (을지대학교 인공지능 융합 시스템 연구실) , 김호철 (을지대학교 일반대학원 보건학과)

Abstract ▼ AI-Helper

Ultrasound examination using ultrasound equipment is an ultrasound device that images human organs using sound waves and is used in various areas such as diagnosis, follow-up, and treatment of diseases. However, if the quality of ultrasound equipment is not guaranteed, the possibility of misdiagnosis increases, and the diagnosis rate decreases. Accordingly, The Korean Society of Radiology and Korea society of Ultrasound in Medicine presented guidelines for quality management of ultrasound equipment using ATS-539 phantom. The DenseNet201 classification algorithm shows 99.25% accuracy and 5.17% loss in the Dead Zone, 97.52% loss in Axial/Lateral Resolution, 96.98% accuracy and 20.64% loss in Sensitivity, 93.44% accuracy and 22.07% loss in the Gray scale and Dynamic Range. As a result, it is the best and is judged to be an algorithm that can be used for quantitative evaluation. Through this study, it can be seen that if quantitative evaluation using artificial intelligence is conducted in the qualitative evaluation item of ultrasonic equipment, the reliability of ultrasonic equipment can be increased with high accuracy.

주제어

표/그림 (11)

그림 그림 1. 초음파영상진단장치(EPIQ 5 ultrasound system, Philips, USA, (a))에 Convex Probe(C5-1, (b)), ATS-539 다목적 초음파 팬텀(ATS 539 Multipurpose Phantom, ATS Laboratories, USA, (c)) Fig. 1. EPIQ 5 utrasound system(Philips, USA, (a)), Convex Probe(C5-1, Philips, USA, (b)), ATS-539 Multipurpose Phantom (ATS Laboratories, USA, (c))
그림 그림 2. ATS-539 팬텀 검사 항목, 불응영역(a), 수직거리측정(b), 수평거리측정(c), 축/측 방향 분해능(d), 초점영역(e), 투과심도(f), 기능적 해상도(g), 회색조와 다이내믹 레인지(h) Fig. 2. ATS-539 phantom Quality inspection items, Dead Zone(a), Vertical Measurement(b), Horizontal Measurement(c), Axial/Lateral Resolution(d), Focal Zone(e), Sensitivity(f), Functional Resolution(g), Gray Scale and Dynamic Range(h)
표 표 1. 획득한 초음파 팬텀 영상의 수 Table 1. Number of Phantom images
그림 그림 3. 불응영역 합격(a), 불합격(b) 초음파 영상 Fig. 3. Dead zone pass(a), non-pass(b) Ultrasound image
그림 그림 4. 축/측 방향 분해능 합격(a), 불합격(b) 초음파 영상 Fig. 4. Axial/Lateral Resolution pass(a), non-pass(b) Ultrasound image
그림 그림 5. 투과심도(a), 불합격(b) 초음파 영상 Fig. 5. Sensitivity pass(a), non-pass(b) Ultrasound image
그림 그림 6. 회색조와 다이내믹 레인지 합격(a), 불합격(b) 초음파 영상 Fig. 6. Gray Scale and Dynamic Range pass(a), non-pass(b) Ultrasound Image
표 표 2. 불응영역 초음파 검사 분류모델 성능 Table 2. DeadZone Learning Performance of the Classification Model
표 표 3. 축/측 방향 분해능 초음파 검사 분류모델 성능 Table 3. Axial/Lateral Resolution Learning Performance of the Classification Model
표 표 4. 투과심도 초음파 검사 분류모델 성능 Table 4. Sensitivity Learning Performance of the Classification Model
표 표 5. 회색조와 다이내믹 레인지 초음파 검사 분류모델 성능 Table 5. Gray Scale and Dynamic Range Learning Performance of the Classification Model

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