수치 모델과 그래프 이론을 이용한 향상된 영상 분할 연구 -폐 영상에 응용-

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 협동과정 바이오엔지니어링전공, 2016. 2. 김희찬.This dissertation presents a thoracic cavity segmentation algorithm and a method of pulmonary artery and vein decomposition from volumetric chest CT, and evaluates their performances. The main contribution of this research is to develop an automated algorithm for segmentation of the clinically meaningful organ. Although there are several methods to improve the organ segmentation accuracy such as the morphological method based on threshold algorithm or the object selection method based on the connectivity information our novel algorithm uses numerical algorithms and graph theory which came from the computer engineering field. This dissertation presents a new method through the following two examples and evaluates the results of the method. The first study aimed at the thoracic cavity segmentation. The thoracic cavity is the organ enclosed by the thoracic wall and the diaphragm surface. The thoracic wall has no clear boundary. Moreover since the diaphragm is the thin surface, this organ might have lost parts of its surface in the chest CT. As the previous researches, a method which found the mediastinum on the 2D axial view was reported, and a thoracic wall extraction method and several diaphragm segmentation methods were also informed independently. But the thoracic cavity volume segmentation method was proposed in this thesis for the first time. In terms of thoracic cavity volumetry, the mean±SD volumetric overlap ratio (VOR), false positive ratio on VOR (FPRV), and false negative ratio on VOR (FNRV) of the proposed method were 98.17±0.84%, 0.49±0.23%, and 1.34±0.83%, respectively. The proposed semi-automatic thoracic cavity segmentation method, which extracts multiple organs (namely, the rib, thoracic wall, diaphragm, and heart), performed with high accuracy and may be useful for clinical purposes. The second study proposed a method to decompose the pulmonary vessel into vessel subtrees for separation of the artery and vein. The volume images of the separated artery and vein could be used for a simulation support data in the lung cancer. Although a clinician could perform the separation in his imagination, and separate the vessel into the artery and vein in the manual, an automatic separation method is the better method than other methods. In the previous semi-automatic method, root marking of 30 to 40 points was needed while tracing vessels under 2D slice view, and this procedure needed approximately an hour and a half. After optimization of the feature value set, the accuracy of the arterial and venous decomposition was 89.71 ± 3.76% in comparison with the gold standard. This framework could be clinically useful for studies on the effects of the pulmonary arteries and veins on lung diseases.Chapter 1 General Introduction 2 1.1 Image Informatics using Open Source 3 1.2 History of the segmentation algorithm 5 1.3 Goal of Thesis Work 8 Chapter 2 Thoracic cavity segmentation algorithm using multi-organ extraction and surface fitting in volumetric CT 10 2.1 Introduction 11 2.2 Related Studies 13 2.3 The Proposed Thoracic Cavity Segmentation Method 16 2.4 Experimental Results 35 2.5 Discussion 41 2.6 Conclusion 45 Chapter 3 Semi-automatic decomposition method of pulmonary artery and vein using two level minimum spanning tree constructions for non-enhanced volumetric CT 46 3.1 Introduction 47 3.2 Related Studies 51 3.3 Artery and Vein Decomposition 55 3.4 An Efficient Decomposition Method 70 3.5 Evaluation 75 3.6 Discussion and Conclusion 85 References 88 Abstract in Korean 95Docto

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Fully automatic segmentation and objective assessment of atrial scars for longstanding persistent atrial fibrillation patients using late gadolinium-enhanced MRI

Purpose: Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed non-invasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualised as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time-consuming and subject to inter-operator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF. Methods: Our fully automatic pipeline uniquely combines: (1) a multi-atlas based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (2) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground-truth segmentations in 37 patients with persistent long standing AF. Results: Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%) respectively compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91. Conclusion: Compared with previously studied methods with manual interventions, our innovative pipeline demonstrated comparable results, but was computed fully automatically. The proposed segmentation methods allow LGE MRI to be used as an objective assessment tool for localisation, visualisation and quantification of atrial scarring and to guide ablation treatment

Interventional techniques in the management of persistent atrial fibrillation

Atrial fibrillation (AF) is a common cardiac rhythm problem experienced by patients and comprises an increasing demand on healthcare systems. AF is characterised by advanced neurohormonal remodelling in the atria resulting in dilation and variable degree of atrial fibrosis that can be measured by imaging techniques with difficulty in developing methods of identifying and quantifying left atrial (LA) fibrosis. LA fibrosis can be estimated by measuring LA scar using non-invasive imaging methods such as strain imaging in advanced echocardiography and in cardiac magnetic resonance (CMR) imaging. Achieving rhythm control strategy utilising catheter ablation (CA) has shown to be advantageous in improving quality of life (QOL) in patients with paroxysmal AF. The most effective method in management of AF has remained elusive in non-paroxysmal AF. Thoracoscopic surgical ablation (TSA) has been developed over the last decade by experienced surgeons with some promising early results but has not been investigated in long-standing persistent AF (LSPAF). I have attempted to answer some of the relevant questions that have remained in management of LSPAF by conducting a multicentre randomised control trial comparing efficacy between CA and TSA (CASA-AF RCT) and improvements in quality of life indices. In a sub-study, I measured LA volumes using echocardiography and CMR to determine reverse remodelling and LA function using tissue Doppler imaging and strain imaging to predict AF recurrence. In a CMR sub-study, a novel automatic LA segmentation algorithm was used to quantify LA fibrosis before and after ablation. I was able to quantify the response of the autonomic nervous system to targeted ganglionic plexi (GP) ablation as part of TSA compared to CA by measuring heart rate variability. I am hopeful that the knowledge gained from this thesis will help with an appropriate selection that will improve the management of patients with LSPAF.Open Acces

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

Coronary Angiography

In the intervening 10 years tremendous advances in the field of cardiac computed tomography have occurred. We now can legitimately claim that computed tomography angiography (CTA) of the coronary arteries is available. In the evaluation of patients with suspected coronary artery disease (CAD), many guidelines today consider CTA an alternative to stress testing. The use of CTA in primary prevention patients is more controversial in considering diagnostic test interpretation in populations with a low prevalence to disease. However the nuclear technique most frequently used by cardiologists is myocardial perfusion imaging (MPI). The combination of a nuclear camera with CTA allows for the attainment of coronary anatomic, cardiac function and MPI from one piece of equipment. PET/SPECT cameras can now assess perfusion, function, and metabolism. Assessing cardiac viability is now fairly routine with these enhancements to cardiac imaging. This issue is full of important information that every cardiologist needs to now