Semi-automated quantification of left ventricular volumes and ejection fraction by real-time three-dimensional echocardiography

<p>Abstract</p> <p>Background</p> <p>Recent studies have shown that real-time three-dimensional (3D) echocardiography (RT3DE) gives more accurate and reproducible left ventricular (LV) volume and ejection fraction (EF) measurements than traditional two-dimensional methods. A new semi-automated tool (4DLVQ) for volume measurements in RT3DE has been developed. We sought to evaluate the accuracy and repeatability of this method compared to a 3D echo standard.</p> <p>Methods</p> <p>LV end-diastolic volumes (EDV), end-systolic volumes (ESV), and EF measured using 4DLVQ were compared with a commercially available semi-automated analysis tool (TomTec 4D LV-Analysis ver. 2.2) in 35 patients. Repeated measurements were performed to investigate inter- and intra-observer variability.</p> <p>Results</p> <p>Average analysis time of the new tool was 141s, significantly shorter than 261s using TomTec (<it>p </it>< 0.001). Bland Altman analysis revealed high agreement of measured EDV, ESV, and EF compared to TomTec (<it>p </it>= <it>NS</it>), with bias and 95% limits of agreement of 2.1 ± 21 ml, -0.88 ± 17 ml, and 1.6 ± 11% for EDV, ESV, and EF respectively. Intra-observer variability of 4DLVQ vs. TomTec was 7.5 ± 6.2 ml vs. 7.7 ± 7.3 ml for EDV, 5.5 ± 5.6 ml vs. 5.0 ± 5.9 ml for ESV, and 3.0 ± 2.7% vs. 2.1 ± 2.0% for EF (<it>p </it>= <it>NS</it>). The inter-observer variability of 4DLVQ vs. TomTec was 9.0 ± 5.9 ml vs. 17 ± 6.3 ml for EDV (<it>p </it>< 0.05), 5.0 ± 3.6 ml vs. 12 ± 7.7 ml for ESV (<it>p </it>< 0.05), and 2.7 ± 2.8% vs. 3.0 ± 2.1% for EF (<it>p </it>= <it>NS</it>).</p> <p>Conclusion</p> <p>In conclusion, the new analysis tool gives rapid and reproducible measurements of LV volumes and EF, with good agreement compared to another RT3DE volume quantification tool.</p

Automated Analysis of 3D Stress Echocardiography

__Abstract__ The human circulatory system consists of the heart, blood, arteries, veins and capillaries. The heart is the muscular organ which pumps the blood through the human body (Fig. 1.1,1.2). Deoxygenated blood flows through the right atrium into the right ventricle, which pumps the blood into the pulmonary arteries. The blood is carried to the lungs, where it passes through a capillary network that enables the release of carbon dioxide and the uptake of oxygen. Oxygenated blood then returns to the heart via the pulmonary veins and flows from the left atrium into the left ventricle. The left ventricle then pumps the blood through the aorta, the major artery which supplies blood to the rest of the body [Drake et a!., 2005; Guyton and Halt 1996]. Therefore, it is vital that the cardiovascular system remains healthy. Disease of the cardiovascular system, if untreated, ultimately leads to the failure of other organs and death

Automated volume measurements in echocardiography by utilizing expert knowledge

Left ventricular (LV) volumes and ejection fraction (EF) are important parameters for diagnosis, prognosis, and treatment planning in patients with heart disease. These parameters are commonly measured by manual tracing in echocardiographic images, a procedure that is time consuming, prone to inter- and intra-observer variability, and require highly trained operators. This is particularly the case in three-dimensional (3D) echocardiography, where the increased amount of data makes manual tracing impractical. Automated methods for measuring LV volumes and EF can therefore improve efficiency and accuracy of echocardiographic examinations, giving better diagnosis at a lower cost. The main goal of this thesis was to improve the efficiency and quality of cardiac measurements. More specifically, the goal was to develop rapid and accurate methods that utilize expert knowledge for automated evaluation of cardiac function in echocardiography. The thesis presents several methods for automated volume and EF measurements in echocardiographic data. For two-dimensional (2D) echocardiography, an atlas based segmentation algorithm is presented in paper A. This method utilizes manually traced endocardial contours in a validated case database to control a snake optimized by dynamic programming. The challenge with this approach is to find the most optimal case in the database. More promising results are achieved in triplane echocardiography using a multiview and multi-frame extension to the active appearance model (AAM) framework, as demonstrated in paper B. The AAM generalizes better to new patient data and is based on more robust optimization schemes than the atlas-based method. In triplane images, the results of the AAM algorithm may be improved further by integrating a snake algorithm into the AAM framework and by constraining the AAM to manually defined landmarks, and this is shown in paper C. For 3D echocardiograms, a clinical semi-automated volume measurement tool with expert selected points is validated in paper D. This tool compares favorably to a reference measurement tool, with good agreement in measured volumes, and with a significantly lower analysis time. Finally, in paper E, fully automated real-time segmentation in 3D echocardiography is demonstrated using a 3D active shape model (ASM) of the left ventricle in a Kalman filter framework. The main advantage of this approach is its processing performance, allowing for real-time volume and EF estimates. Statistical models such as AAMs and ASMs provide elegant frameworks for incorporating expert knowledge into segmentation algorithms. Expert knowledge can also be utilized directly through manual input to semi-automated methods, allowing for manual initialization and correction of automatically determined volumes. The latter technique is particularly suitable for clinical routine examinations, while the fully automated 3D ASM method can extend the use of echocardiography to new clinical areas such as automated patient monitoring. In this thesis, different methods for utilizing expert knowledge in automated segmentation algorithms for echocardiography have been developed and evaluated. Particularly in 3D echocardiography, these contributions are expected to improve efficiency and quality of cardiac measurements

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

Bi-temporal 3D active appearance models with applications to unsupervised ejection fraction estimation

Rapid and unsupervised quantitative analysis is of utmost importance to ensure clinical acceptance of many examinations using cardiac magnetic resonance imaging (MRI). We present a framework that aims at fulfilling these goals for the application of left ventricular ejection fraction estimation in four-dimensional MRI. The theoretical foundation of our work is the generative two-dimensional Active Appearance Models by Cootes et al., here extended to bi-temporal, three-dimensional models. Further issues treated include correction of respiratory induced slice displacements, systole detection, and a texture model pruning strategy. Cross-validation carried out on clinical-quality scans of twelve volunteers indicates that ejection fraction and cardiac blood pool volumes can be estimated automatically and rapidly with accuracy on par with typical inter-observer variability. \u

Foetal echocardiographic segmentation

Congenital heart disease affects just under one percentage of all live births [1]. Those defects that manifest themselves as changes to the cardiac chamber volumes are the motivation for the research presented in this thesis. Blood volume measurements in vivo require delineation of the cardiac chambers and manual tracing of foetal cardiac chambers is very time consuming and operator dependent. This thesis presents a multi region based level set snake deformable model applied in both 2D and 3D which can automatically adapt to some extent towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts. The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD). The level set methods presented in this thesis have an optional shape prior term for constraining the segmentation by a template registered to the image in the presence of shadowing and heavy noise. When applied to real data in the absence of the template the MSSCD algorithm is initialised from seed primitives placed at the centre of each cardiac chamber. The voxel statistics inside the chamber is determined before evolution. The MSSCD stops at open boundaries between two chambers as the two approaching level set fronts meet. This has significance when determining volumes for all cardiac compartments since cardiac indices assume that each chamber is treated in isolation. Comparison of the segmentation results from the implemented snakes including a previous level set method in the foetal cardiac literature show that in both 2D and 3D on both real and synthetic data, the MSSCD formulation is better suited to these types of data. All the algorithms tested in this thesis are within 2mm error to manually traced segmentation of the foetal cardiac datasets. This corresponds to less than 10% of the length of a foetal heart. In addition to comparison with manual tracings all the amorphous deformable model segmentations in this thesis are validated using a physical phantom. The volume estimation of the phantom by the MSSCD segmentation is to within 13% of the physically determined volume

Automated analysis of 3D echocardiography

In this thesis we aim at automating the analysis of 3D echocardiography, mainly targeting the functional analysis of the left ventricle. Manual analysis of these data is cumbersome, time-consuming and is associated with inter-observer and inter-institutional variability. Methods for reconstruction of 3D echocardiographic images from fast rotating ultrasound transducers is presented and methods for analysis of 3D echocardiography in general, using tracking, detection and model-based segmentation techniques to ultimately fully automatically segment the left ventricle for functional analysis. We show that reliable quantification of left ventricular volume and mitral valve displacement can be achieved using the presented techniques.SenterNovem (IOP Beeldverwerking, grant IBVC02003), Dutch Technology Foundation STW (grant 06666)UBL - phd migration 201

임상의사 결정 지원 시스템을 위한 심층 신경망 기반의 심초음파 자동해석에 관한 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2022. 8. 김희찬.심초음파 검사는 심장병 진단에 사용되는 중요한 도구이며, 수축기 및 이완기 단계의 심장 이미지를 제공한다. 심초음파 검사를 통해 심방과 심실의 다양한 구조적 이상과, 판막 이상등의 질환을 정량적으로 또는 정성적으로 진단할 수 있다. 심초음파 검사는 비침습적인 특성으로 인하여에 심장 전문의들이 많이 사용하고 있으며, 심장 질환자가 점점 많아지는 추세에 따라 더 많이 사용될 것으로 기대되고 있다. 심초음파 검사는 이러한 안전성과 유용성에도 불구하고, CT나 MRI와는 달리 1)정확한 영상을 얻는데 오랜 훈련기간이 필요하고 2) 영상을 얻을 수 있는 부위와 얻을 수 잇는 단면영상이 제한적이어서 검사 시 놓친 소견은 추후 영상을 감수할 경우에도 발견할 수 없는 특징을 가지고 있다. 이에 다라 측정과 해석의 정량화와 함께 검사상 이상소견을 놓치지 않을 수 있는 보완조치에 대한 요구가 많았고, 이러한 요구에 부응하여 심장전문의를 위한 임상 의사결정 지원 시스템에 대한 많은 연구가 진행되고 있다.. 인공지능의 발달로 인해 어느정도 이러한 요구에 부응할 수 있게 되었다. 이 연구의 흐름은 두가지로 나뉘게 되는데, 첫째는 심장의 구조물들을 분할하여 크기를 측정하고 특이치를 감지하는 정량적인 연구방법과, 병변이 어느 부위에 있는지 이미지 내에서 확인하는 정성적 접근법으로 나뉜다. 기존에는 이 두 연구가 대부분 따로 진행되어 왔으나, 임상의사의 진단 흐름을 고려해 볼 때 이 두가지 모두가 포함되는 임상 의사 결정 지원 시스템의 개발이 필요한 현실이다. 이러한 관점에서 본 학위 논문의 목표는 대규모 코호트 후향적 연구를 통해 AI 기반의 심장 초음파 임상 의사결정 지원 시스템을 개발하고 검증하는 것이다. 데이터는 2016년에서 2021년도 사이에 서울대 병원에서 시행된 2600예의 심초음파검사 영상(정상소견1300명, 병적소견 1300명)를 이용하였다. 정량적분석과 정성적 분석을 모두 고려하기 위해 두개의 네트워크가 개발되었으며, 그 유효성은 환자 데이터로 검증되었다. 먼저 정량적 분석을 위한 이미지 분할을 위해 U-net 기반 딥러닝 네트워크가 개발되었으며, 개발에 필요한 데이터를 위해 심장전문의가 좌심실, 좌심방, 대동맥, 우심실, 좌심실 후벽 및 심실간 중격의 정보를 이미지에 표시를 하였다. 훈련된 네트워크로부터 나온 이미지로부터 6개의 구조물의 직경과 면적을 구하여 벡터화 하였으며, 수축기말 및 이완기말 단계의 프레임 정보를 벡터로부터 추출하였다. 둘째로 정성적 진단을 위한 네트워크 개발을 위해 Resnet152 기반의 CNN을 사용하였다. 이 네트워크의 입력데이터는 정량적 네트워크에서 추출된 수축기말 및 이완기말 정보를 기반으로 10프레임이 추출되었다. 입력데이터가 정상인지 아닌지 구분하도록 했을 뿐 아니라, 마지막 레이어에서 그라디언트 가중 클래스 활성화 매핑(Grad-CAM)방법론을 이용하여 네트워크가 이미지상의 어느 부위를 보고 이상소견으로 분류했는지 시각화 하였다. 그 결과 먼저 정량적 네트워크 성능을 측정하기 위해 환자 1300명의 데이터를 통해 각 구조물의 직경과 관련된 심장질환이 얼마나 잘 검출됐는지 확인하였다. 심실중격, 좌심실 후벽, 대동맥과 관련된 병적소견을 제외하고 다른구조물의 민감도와 특이성은 모두 90% 이상이다. 수축기 말기 및 확장기 말기 위상 검출도 정확했는데, 심장전문의에 의해 선택된 프레임에 비하여 수축기 말기의 경우 평균 0.52 프레임, 확장기 말기의 경우 0.9 프레임의 차이를 보였다. 정성분석을 위한 네트워크의 경우, 첫 번째 네트워크로부터 선택된 위상정보를 바탕으로 10개의 입력데이터를 결정하였고, 무작위로 선택된 10개의 결과를 비교하였다. 그 결과 정확도가 각각 90.33%, 81.16%로 나타났으며, 1차 정량적 네트워크 에서 추출된 수축기말, 이완기말 프레임 정보는 환자를 판별하는 네트워크의 성능 향상에 기여했음을 알 수 있다. 또한 Grad-CAM 결과는 첫 번째 네트워크의 프레임 정보를 기반으로 데이터에서 추출된10 장의 이미지가 입력데이터로 쓰였을 때가 무작위로 추출된 10장의 이미지로 훈련된 네트워크 보다 병변의 위치를 더 정확하게 표시하는 것을 확인하였다. 결론적으로 본 연구는 정량적, 정성적 분석을 위한 AI 기반 심장 초음파 임상의사 결정 지원 시스템을 개발하였으며, 이 시스템이 실현 가능한 것으로 검증되었다.Echocardiography is an indispensable tool for cardiologists in the diagnosis of heart diseases. By echocardiography, various structural abnormalities in the heart can be quantitatively or qualitatively diagnosed. Due to its non-invasiveness, the usage of echocardiography in the diagnosis of heart disease has continuously increased. Despite the increasing role in the cardiology practice, echocardiography requires experience in capturing and knowledge in interpreting images. . Moreover, in contrast to CT or MRI images, important information can be missed once not obtained at the time of examination. Therefore, obtaining and interpreting images should be done simultaneously, or, at least, all obtained images should be audited by the experienced cardiologist before releasing the patient from the examination booth. Because of the peculiar characteristics of echocardiography compared to CT or MRI, there have been incessant demands for the clinical decision support system(CDSS) for echocardiography. With the advance of Artificial Intelligence (AI), there have been several studies regarding decision support systems for echocardiography. The flow of these studies is divided into two approaches: One is the quantitative approach to segment the images and detects an abnormality in size and function. The other is the qualitative approach to detect abnormality in morphology. Unfortunately, most of these two studies have been conducted separately. However, since cardiologists perform quantitative and qualitative analysis simultaneously in analyzing echocardiography, an optimal CDSS needs to be a combination of these two approaches. From this point of view, this study aims to develop and validate an AI-based CDSS for echocardiograms through a large-scale retrospective cohort. Echocardiographic data of 2,600 patients who visited Seoul National University Hospital (1300 cardiac patients and 1300 non-cardiac patients with normal echocardiogram) between 2016 and 2021. Two networks were developed for the quantitative and qualitative analysis, and their usefulnesses were verified with the patient data. First, a U-net based deep learning network was developed for segmentation in the quantitative analysis. Annotated images by the experienced cardiologist with the left ventricle, interventricular septum, left ventricular posterior wall, right ventricle, aorta, and left atrium, were used for training. The diameters and areas of the six structures were obtained and vectorized from the segmentation images, and the frame information at the end-systolic and end-diastolic phases was extracted from the vector. The second network for the qualitative diagnosis was developed using a convolutional neural network (CNN) based on Resnet 152. The input data of this network was extracted from 10 frames of each patient based on end-diastolic and end-systolic phase information extracted from the quantitative network. The network not only distinguished the input data between normal and abnormal but also visualized the location of the abnormality on the image through the Gradient-weighted Class Activation Mapping (Grad-CAM) at the last layer. The performance of the quantitative network in the chamber size and function measurements was assessed in 1300 patients. Sensitivity and specificity were both over 90% except for pathologies related to the left ventricular posterior wall, interventricular septum, and aorta. The end-systolic and end-diastolic phase detection was also accurate, with an average difference of 0.52 frames for the end-systolic and 0.9 frames for the end-diastolic phases. In the case of the network for qualitative analysis, 10 input data were selected based on the phase information determined from the first network, and the results of 10 randomly selected images were compared. As a result, the accuracy was 90.3% and 81.2%, respectively, and the phase information selected from the first network contributed to the improvement of the performance of the network. Also, the results of Grad-CAM confirmed that the network trained with 10 images of data extracted based on the phase information from the first network displays the location of the lesion more accurately than the network trained with 10 randomly selected data. In conclusion, this study proposed an AI-based CDSS for echocardiography in the quantitative and qualitative analysis.ABSTRACT １ CONTENTS v LIST OF TABLES vii LIST OF FIGURES viii CHAPTER 1 1 Introduction 1 1. Introduction 2 1.1. Echocardiogram 2 1.1.1. Diagnosis using Echocardiogram 2 1.1.2. Limitation in Echocardiogram 3 1.1.3. Artificial Intelligence in Echocardiogram 6 1.2. Clinical Background 7 1.2.1. Diagnostic Flow 8 1.2.2. Previous studies and clinical implication of this study 11 1.3. Technical Background 16 1.3.1. Convolutional Neural Network (CNN) 16 1.3.1.1. U-net 18 1.3.1.2. Residual Network 20 1.3.1.3. Gradient-weighted Class Activation Mapping (Grad-CAM) 22 1.4. Unmet Clinical Needs 26 1.5. Objective 27 CHAPTER 2 28 Materials & Methods 28 2. Materials & Methods 29 2.1. Data Description 29 2.2. Annotated Data 32 2.3. Overall Architecture 33 2.3.1. Quantitative Network 35 2.3.2. Qualitative Network 37 2.4. Dice Similarity Score 39 2.5. Intersection over Union 40 CHAPTER 3 41 3. Results & Discussion 42 3.1. Quantitative Network Result 42 3.1.1. Diagnostic results 47 3.1.2. Phase Detection Result 49 3.2. Qualitative Network Results 51 3.2.1. Grad-CAM Result 56 3.3. Limitation 58 3.3.1. Need for external dataset for generalizable network 58 3.3.2. Futurework of the system 59 CHAPTER 4 60 4. Conclusion 61 Abstract in Korean 62 Bibliography 65박

Use of Image Processing Techniques for the Analysis of Echocardiographic Images

Echocardiography is a medical imaging modality that uses ultrasound in order to obtain cross sectional views of the heart. The basic problem in the use of echocardiography is the ability to obtain a reliable set of physical parameters related to cardiac status, so that assessment of heart disease can be performed automatically. This work overviews different image processing techniques used in the analysis of two dimensional echocardiographic images. After reviewing how the echocardiographic image formation process works, an outline of the general processing steps from image acquisition to automatic detection of important features is presented. Special emphasis on cardiac image segmentation is presented. In particular, a relaxation algorithm for image segmentation is discussed. Also, echocardiographic image segmentation using temporal analysis and a new algorithm for boundary detection is described. Measurements of left ventricular area, wall thickness, and ejection fraction is also presented. Shape analysis is introduced as a tool for echocardiographic image analysis. A high level description of the left ventricular boundaries using curvature is proposed. Curvature analysis attempts to identify stable landmarks during the beating process, muscles. Tracking these landmarks aids in the detection of abnormal heart contractions. Finally the use of expert systems is proposed in the analysis of echocardiographic images