3D MODELLING AND RAPID PROTOTYPING FOR CARDIOVASCULAR SURGICAL PLANNING – TWO CASE STUDIES

In the last years, cardiovascular diagnosis, surgical planning and intervention have taken advantages from 3D modelling and rapid prototyping techniques. The starting data for the whole process is represented by medical imagery, in particular, but not exclusively, computed tomography (CT) or multi-slice CT (MCT) and magnetic resonance imaging (MRI). On the medical imagery, regions of interest, i.e. heart chambers, valves, aorta, coronary vessels, etc., are segmented and converted into 3D models, which can be finally converted in physical replicas through 3D printing procedure. In this work, an overview on modern approaches for automatic and semiautomatic segmentation of medical imagery for 3D surface model generation is provided. The issue of accuracy check of surface models is also addressed, together with the critical aspects of converting digital models into physical replicas through 3D printing techniques. A patient-specific 3D modelling and printing procedure (Figure 1), for surgical planning in case of complex heart diseases was developed. The procedure was applied to two case studies, for which MCT scans of the chest are available. In the article, a detailed description on the implemented patient-specific modelling procedure is provided, along with a general discussion on the potentiality and future developments of personalized 3D modelling and printing for surgical planning and surgeons practice

Multi-label segmentation of images with partition trees

We propose a new framework for multi-class image segmentation with shape priors using a binary partition tree. In the literature, such trees are used to represent hierarchical partitions of images, and are usually computed in a bottom-up manner based on color similarities, then analyzed to detect objects with a known shape prior. However, not considering shape priors during the construction phase induces mistakes in the later segmentation. This paper proposes a method which uses both color distribution and shape priors to optimize the trees for image segmentation. The method consists in pruning and regrafting tree branches in order to minimize the energy of the best segmentation that can be extracted from the tree. Theoretical guarantees help reducing the search space and make the optimization efficient. Our experiments show that the optimization approach succeeds in incorporating shape information into multi-label segmentation, outperforming the state-of-the-art

Visualization of the Cardiac Excitation and PVC Arrhythmia on a 3D Heart Model

Visualization of the cardiac potential movement is important in understanding the physiology of the human heart. A 3D visualization tool will help the cardiology students and others interested in human physiology to understand the functioning of the heart. In this thesis, such a tool is proposed which helps in the visualization of the cardiac potential movement and Premature Ventricular Contraction (PVC) event on a 3D heart model. The cardiac excitation obtained from a limb lead and a precordial lead of a 12 lead electrocardiograph (ECG) is mapped on a 3D heart model with fixed conduction pathways. The 3D heart model is obtained by modifying an existing anatomically accurate heart model. Fixed conduction pathways are defined on this derived 3D heart model. Each component of the ECG corresponds to the potential movement along each segment of these conduction pathways. The timing information from the limb lead signal is used to map the position of the cardiac potential on these conduction pathways. Amplitude and the timing information obtained from the precordial lead is mapped on a vector which points towards the corresponding precordial electrode on a separate window. This helps in understanding the instantaneous position of the cardiac potential on the transverse plane. Mapping of the cardiac excitation on the conduction pathways will stop and the color map of the heart will change during the occurrence of a PVC event. MIT-BIH arrhythmia database signals with at least one PVC wave were considered as input signal. It is observed that the system was able to detect PVC approximately 95% of the time (for the selected sample signals) and was able to map each ECG component accurately on the conduction pathways with minimum mapping delay

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

심장 컴퓨터 단층촬영 영상으로부터 경사도 보조 지역 능동 윤곽 모델을 이용한 심장 영역 자동 분할 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 신영길.The heart is one of the most important human organs, and composed of complex structures. Computed tomography angiography (CTA), magnetic resonance imaging (MRI), and single photon emission computed tomography are widely used, non-invasive cardiac imaging modalities. Compared with other modalities, CTA can provide more detailed anatomic information of the heart chambers, vessels, and coronary arteries due to its higher spatial resolution. To obtain important morphological information of the heart, whole heart segmentation is necessary and it can be used for clinical diagnosis. In this paper, we propose a novel framework to segment the four chambers of the heart automatically. First, the whole heart is coarsely extracted. This is separated into the left and right parts using a geometric analysis based on anatomical information and a subsequent power watershed. Then, the proposed gradient-assisted localized active contour model (GLACM) refines the left and right sides of the heart segmentation accurately. Finally, the left and right sides of the heart are separated into atrium and ventricle by minimizing the proposed split energy function that determines the boundary between the atrium and ventricle based on the shape and intensity of the heart. The main challenge of heart segmentation is to extract four chambers from cardiac CTA which has weak edges or separators. To enhance the accuracy of the heart segmentation, we use region-based information and edge-based information for the robustness of the accuracy in heterogeneous region. Model-based method, which requires a number of training data and proper template model, has been widely used for heat segmentation. It is difficult to model those data, since training data should describe precise heart regions and the number of data should be high in order to produce more accurate segmentation results. Besides, the training data are required to be represented with remarkable features, which are generated by manual setting, and these features must have correspondence for each other. However in our proposed methods, the training data and template model is not necessary. Instead, we use edge, intensity and shape information from cardiac CTA for each chamber segmentation. The intensity information of CTA can be substituted for the shape information of the template model. In addition, we devised adaptive radius function and Gaussian-pyramid edge map for GLACM in order to utilize the edge information effectively and improve the accuracy of segmentation comparison with original localizing region-based active contour model (LACM). Since the radius of LACM affects the overall segmentation performance, we proposed an energy function for changing radius adaptively whether homogeneous or heterogeneous region. Also we proposed split energy function in order to segment four chambers of the heart in cardiac CT images and detects the valve of atrium and ventricle. In experimental results using twenty clinical datasets, the proposed method identified the four chambers accurately and efficiently. We also demonstrated that this approach can assist the cardiologist for the clinical investigations and functional analysis.Contents Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Goal 7 1.3 Main Contribtions 9 1.4 Organization of the Dissertation 10 Chapter 2 Related Works 11 2.1 Medical Image Segmentation 11 2.1.1 Classic Methods 11 2.1.2 Variational Methods 15 2.1.3 Image Features of the Curve 21 2.1.4 Combinatorial Methods 25 2.1.5 Difficulty of Segmentation 30 2.2 Heart Segmentation 33 2.2.1 Non-Model-Based Segmentation 34 2.2.2 Unstatistical Model-Based Segmentation 35 2.2.3 Statistical Model-Based Segmentation 37 Chapter 3 Gradient-assisted Localized Active Contour Model 41 3.1 LACM 41 3.2 Gaussian-pyramid Edge Map 46 3.3 Adaptive Radius Function 50 3.4 LACM with Gaussian-pyramid Edge Map and Adaptive Radius Function 52 Chapter 4 Segmentation of Four Chambers of Heart 54 4.1 Overview 54 4.2 Segmentation of Whole Heart 56 4.3 Separation of Left and Right Sides of Heart 59 4.3.1 Extraction of Candidate Regions of LV and RV 60 4.3.2 Detection of Left and Right sides of Heart 62 4.4 Segmentation of Left and Right Sides of Heart 66 4.5 Separation of Atrium and Ventricle from Heart 69 4.5.1 Calculation of Principal Axes of Left and Right Sides of Heart 69 4.5.2 Detection of Separation Plane Using Split Energy Function 70 Chapter 5 Experiments 74 5.1 Performance Evaluation 74 5.2 Comparison with Conventional Method 79 5.3 Parametric Study 84 5.4 Computational Performance 85 Chapter 6 Conclusion 86 Bibliography 89Docto

Evaluating and Improving 4D-CT Image Segmentation for Lung Cancer Radiotherapy

Lung cancer is a high-incidence disease with low survival despite surgical advances and concurrent chemo-radiotherapy strategies. Image-guided radiotherapy provides for treatment measures, however, significant challenges exist for imaging, treatment planning, and delivery of radiation due to the influence of respiratory motion. 4D-CT imaging is capable of improving image quality of thoracic target volumes influenced by respiratory motion. 4D-CT-based treatment planning strategies requires highly accurate anatomical segmentation of tumour volumes for radiotherapy treatment plan optimization. Variable segmentation of tumour volumes significantly contributes to uncertainty in radiotherapy planning due to a lack of knowledge regarding the exact shape of the lesion and difficulty in quantifying variability. As image-segmentation is one of the earliest tasks in the radiotherapy process, inherent geometric uncertainties affect subsequent stages, potentially jeopardizing patient outcomes. Thus, this work assesses and suggests strategies for mitigation of segmentation-related geometric uncertainties in 4D-CT-based lung cancer radiotherapy at pre- and post-treatment planning stages