• Korean Journal of Radiology
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• 제22권6호
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• pp.901-911
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• 2021

Objective: To evaluate the technical applicability of a semiautomatic three-dimensional (3D) hybrid CT segmentation method for the quantification of right ventricular mass in patients with cardiovascular disease. Materials and Methods: Cardiac CT (270 cardiac phases) was used to quantify right ventricular mass using a semiautomatic 3D hybrid segmentation method in 195 patients with cardiovascular disease. Data from 270 cardiac phases were divided into subgroups based on the extent of the segmentation error (no error; ≤ 10% error; > 10% error [technical failure]), defined as discontinuous areas in the right ventricular myocardium. The reproducibility of the right ventricular mass quantification was assessed. In patients with no error or < 10% error, the right ventricular mass was compared and correlated between paired end-systolic and end-diastolic data. The error rate and right ventricular mass were compared based on right ventricular hypertrophy groups. Results: The quantification of right ventricular mass was technically applicable in 96.3% (260/270) of CT data, with no error in 54.4% (147/270) and ≤ 10% error in 41.9% (113/270) of cases. Technical failure was observed in 3.7% (10/270) of cases. The reproducibility of the quantification was high (intraclass correlation coefficient = 0.999, p < 0.001). The indexed mass was significantly greater at end-systole than at end-diastole (45.9 ± 22.1 g/m² vs. 39.7 ± 20.2 g/m², p < 0.001), and paired values were highly correlated (r = 0.96, p < 0.001). Fewer errors were observed in severe right ventricular hypertrophy and at the end-systolic phase. The indexed right ventricular mass was significantly higher in severe right ventricular hypertrophy (p < 0.02), except in the comparison of the end-diastolic data between no hypertrophy and mild hypertrophy groups (p > 0.1). Conclusion: CT quantification of right ventricular mass using a semiautomatic 3D hybrid segmentation is technically applicable with high reproducibility in most patients with cardiovascular disease.

• Korean Journal of Radiology
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• 제20권1호
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• pp.102-113
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• 2019

Objective: To assess the accuracy and potential bias of computed tomography (CT) ventricular volumetry using semiautomatic three-dimensional (3D) threshold-based segmentation in repaired tetralogy of Fallot, and to compare them to those of two-dimensional (2D) magnetic resonance imaging (MRI). Materials and Methods: This retrospective study evaluated 32 patients with repaired tetralogy of Fallot who had undergone both cardiac CT and MRI within 3 years. For ventricular volumetry, semiautomatic 3D threshold-based segmentation was used in CT, while a manual simplified contouring 2D method was used in MRI. The indexed ventricular volumes were compared between CT and MRI. The indexed ventricular stroke volumes were compared with the indexed arterial stroke volumes measured using phase-contrast MRI. The mean differences and degrees of agreement in the indexed ventricular and stroke volumes were evaluated using Bland-Altman analysis. Results: The indexed end-systolic (ES) volumes showed no significant difference between CT and MRI (p > 0.05), while the indexed end-diastolic (ED) volumes were significantly larger on CT than on MRI (93.6 ± 17.5 mL/m² vs. 87.3 ± 15.5 mL/m² for the left ventricle [p < 0.001] and 177.2 ± 39.5 mL/m² vs. 161.7 ± 33.1 mL/m² for the right ventricle [p < 0.001], respectively). The mean differences between CT and MRI were smaller for the indexed ES volumes (2.0-2.5 mL/m²) than for the indexed ED volumes (6.3-15.5 mL/m²). CT overestimated the stroke volumes by 14-16%. With phase-contrast MRI as a reference, CT (7.2-14.3 mL/m²) showed greater mean differences in the indexed stroke volumes than did MRI (0.8-3.3 mL/m²; p < 0.005). Conclusion: Compared to 2D MRI, CT ventricular volumetry using semiautomatic 3D threshold-based segmentation provides comparable ES volumes, but overestimates the ED and stroke volumes in patients with repaired tetralogy of Fallot.

• Korean Journal of Radiology
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• 제21권6호
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• pp.660-669
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• 2020

Objective: To evaluate the accuracy of a deep learning-based automated segmentation of the left ventricle (LV) myocardium using cardiac CT. Materials and Methods: To develop a fully automated algorithm, 100 subjects with coronary artery disease were randomly selected as a development set (50 training / 20 validation / 30 internal test). An experienced cardiac radiologist generated the manual segmentation of the development set. The trained model was evaluated using 1000 validation set generated by an experienced technician. Visual assessment was performed to compare the manual and automatic segmentations. In a quantitative analysis, sensitivity and specificity were calculated according to the number of pixels where two three-dimensional masks of the manual and deep learning segmentations overlapped. Similarity indices, such as the Dice similarity coefficient (DSC), were used to evaluate the margin of each segmented masks. Results: The sensitivity and specificity of automated segmentation for each segment (1-16 segments) were high (85.5-100.0%). The DSC was 88.3 ± 6.2%. Among randomly selected 100 cases, all manual segmentation and deep learning masks for visual analysis were classified as very accurate to mostly accurate and there were no inaccurate cases (manual vs. deep learning: very accurate, 31 vs. 53; accurate, 64 vs. 39; mostly accurate, 15 vs. 8). The number of very accurate cases for deep learning masks was greater than that for manually segmented masks. Conclusion: We present deep learning-based automatic segmentation of the LV myocardium and the results are comparable to manual segmentation data with high sensitivity, specificity, and high similarity scores.

• 한국음향학회지
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• 제28권1호
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• pp.32-43
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• 2009

본 논문은 자동 분할과 extreme learning machine (ELM)을 이용하여 연속 심음신호에 의한 심장질환 분류의 성능을 개선한다. 자동 분할을 위한 전처리 단계에서 비정상적인 심음신호는 심잡음 (murmur)과 클릭음 (click)을 포함하고 있기 때문에 제1음 (S1)과 제2음 (S2) 시작점 검출 결과가 부정확하거나 누락되어 기존의 심장질환 분류 시스템의 정확도를 저하시키게된다. 이러한 분할 오류에 의한 성능 저하를 감소하기 위해 S1 및 S2의 위치를 찾고, S1 및 S2의 시간 차이를 이용하여 부정확한 시작점을 교정한 다음 한 주기 심음 신호를 추출한다. 특징벡터로는 단일 주기의 심음 신호로부터 추출된 멜척도 필터뱅크 로그 에너지 계수와 포락선을 사용한다. 심장질환을 분류하기 위하여 한 개의 은닉층을 가진 ELM 알고리듬을 사용한다. 9가지 심장질환 분류 실험을 수행한 결과, 제안 방법은 81.6%의 분류 정확도를 나타내며, multi-layer perceptron(MLP), support vector machine (SVM), hidden Markov model (HMM) 중에서 가장 높은 분류 정확도를 보여준다.

• 한국정보과학회논문지:소프트웨어및응용
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• 제33권4호
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• pp.418-426
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• 2006

본 논문에서는 수술 도중에 심장내부로 삽입한 초음파 탐침을 통해 획득된 초음파 심장영상에서 강인하게 심장 영역을 고속 자동 분할하는 방법을 제안한다 제안한 방법은 심장 초기 경계 추출, 신뢰도 경쟁을 통한 전체 경계 검출, 회전 국부 방사선 기법을 이용한 국부 경계 보완으로 세 단계로 구성된다. 첫째, 초음파 탐침의 중심에서 방사선을 만들어 각 방사선에서 밝기값 기반 임계값 기법으로 얻어진 심장외부 영역을 이용하여 대략적인 초기 심장영역의 경계를 추출한다. 둘째, 각각의 방사선에서 임계치로 추출된 초기 심장영역의 위치를 포함하여 경계와 영역정보를 이용해 추출된 새로운 후보들과 신뢰도의 경쟁을 수행하여 높은 신뢰성을 가진 심장 경계를 검출한다. 셋째, 방사선 기법으로 경계획득이 어려운 심장의 오목한 영역에서 경계를 따라 회전하면서 국부적으로 방사선 조사법을 적용하여 경계를 보완한다. 제안된 방법은 실제 환자의 심장 수술 도중에 얻어진 초음파 영상에 적용되어 고무적인 결과를 획득했다.

• 대한방사선기술학회지:방사선기술과학
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• 제45권2호
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• pp.151-158
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• 2022

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.

• International journal of advanced smart convergence
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• 제13권2호
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• pp.187-194
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• 2024

For precise cardiac diagnostics and treatment, we introduce a novel method for patient-specific mapping between myocardial and coronary anatomy, leveraging local variations in myocardial thickness. This complex system integrates and automates multiple sophisticated components, including left ventricle segmentation, myocardium segmentation, long-axis estimation, coronary artery tracking, and advanced geodesic Voronoi distance mapping. It meticulously accounts for variations in myocardial thickness and precisely delineates the boundaries between coronary territories according to the conventional 17-segment myocardial model. Each phase of the system provides a step-by-step approach to automate coronary artery mapping onto the myocardium. This innovative method promises to transform cardiac imaging by offering highly precise, automated, and patient-specific analyses, potentially enhancing the accuracy of diagnoses and the effectiveness of therapeutic interventions for various cardiac conditions.

• 한국정보과학회논문지:소프트웨어및응용
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• 제36권10호
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• pp.825-835
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• 2009

의학기술이 발전하면서 결핵, 폐렴, 영양실조, A형간염 등의 질병에 의한 사망률은 감소하는 반면, 심장 질환으로 인한 사망률은 증가하는 추세이다. 심장병을 예방하기 위하여 정기적인 검사가 중요하고, 인체에 무해한 자기공명영상을 활용하여 심장의 혈류량과 심박구출률을 계산하여 심장의 기능을 분석할 필요가 있다. 본 논문에서는 기존의 노동집약적이고 시간적 비용이 큰 수동윤곽분할을 대체하기 위한 자동 좌심실 분할 알고리즘을 제안하였다. 방사형 임계치 결정법을 통하여 심실을 분할하고 혈류량 및 심박구출률을 계산하였으며, 특히 기존 방법들에서 문제가 되었던 기저 영상도 사용자 간섭률을 최소화하여 자동분할을 수행하였다. 제안 알고리즘의 검증을 위하여 36명의 심장 자기공명영상 데이터를 사용하여 전문가에 의한 수동윤곽분할 및 제너럴일렉트로닉스 MASS 소프트웨어와 정량적 비교를 수행하였다. 실험을 통해 제안한 방법이 표준으로 간주되는 수동윤곽분할과 정확도가 유사하며, MASS 소프트웨어보다 높은 정확도를 갖고 있음을 알 수 있었다.

• 정보처리학회논문지B
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• 제15B권6호
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• pp.507-516
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• 2008

최근 연구 결과에 따르면 여러가지 질환 중에 심장 질환으로 인한 사망률이 가장 높은 것으로 나타났다. 임상 실습에서 심장 기능은 좌심실을 수동윤곽검출하여 혈류량이나 심박구출률을 계산하여 분석하지만, 많은 시간과 비용을 필요로 한다. 본 연구에서는 심장을 촬영한 단축 자기공명영상을 사용하여 자동 좌심실 분할 알고리즘을 제안한다. 코일 위치에 따른 왜곡을 보상하고, 에지 정보를 검출하고 특성에 따라 분류한후에, 영역 확장 기법을 사용하여 좌심실을 분할하였다. 또한 부분 복셀소(voxel)의 영향을 고려하였다. 코넬대학교 IRB의 승인하에 38 명의 심장 자기공명영상을 사용하여 제안한 알고리즘을 수동윤곽검출 및 GE MASS 소프트웨어와 비교하였다. 심장의 이완기와 수축기에 혈류량은 부분 복셀소 영향을 고려하지 않을 경우 각각 $3.3mL{\pm}5.8$(표준편차)와 $3.2mL{\pm}4.3$, 부분 복셀소 영향을 고려한 경우 각각 $19.1mL{\pm}8.8$와 $10.3mL{\pm}6.1$의 정확도를 보였다. 심박구출률은 부분 복셀소 영향을 고려하지 않은 경우와 고려한 경우에 대해서 각각 $-1.3%{\pm}2.6$ 와 $-2.1%{\pm}2.4$의 정확도를 보였다. 이를 통해 제안한 알고리즘이 정확하고 정기적인 임상 실습에 유용함을 확인할 수 있다.

• International journal of advanced smart convergence
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• 제7권2호
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• pp.1-6
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• 2018

In this paper, we propose an automatic left ventricle segmentation method in computed tomography angiography (CTA) using separating energy function. First, we smooth the images by applying anisotropic diffusion filter to remove noise. Secondly, the volume of interest (VOI) is detected by using k-means clustering. Thirdly, we divide the left and right heart with split energy function. Finally, we extract only left ventricle from left and right heart with optimizing cost function including orientation term.