Gap Filling of 3-D Microvascular Networks by Tensor Voting

We present a new algorithm which merges discontinuities in 3-D images of tubular structures presenting undesirable gaps. The application of the proposed method is mainly associated to large 3-D images of microvascular networks. In order to recover the real network topology, we need to fill the gaps between the closest discontinuous vessels. The algorithm presented in this paper aims at achieving this goal. This algorithm is based on the skeletonization of the segmented network followed by a tensor voting method. It permits to merge the most common kinds of discontinuities found in microvascular networks. It is robust, easy to use, and relatively fast. The microvascular network images were obtained using synchrotron tomography imaging at the European Synchrotron Radiation Facility. These images exhibit samples of intracortical networks. Representative results are illustrated

Segmentation and reconstruction of 3D artery models for surgical planning

Master'sMASTER OF SCIENC

Inferring Geodesic Cerebrovascular Graphs: Image Processing, Topological Alignment and Biomarkers Extraction

A vectorial representation of the vascular network that embodies quantitative features - location, direction, scale, and bifurcations - has many potential neuro-vascular applications. Patient-specific models support computer-assisted surgical procedures in neurovascular interventions, while analyses on multiple subjects are essential for group-level studies on which clinical prediction and therapeutic inference ultimately depend. This first motivated the development of a variety of methods to segment the cerebrovascular system. Nonetheless, a number of limitations, ranging from data-driven inhomogeneities, the anatomical intra- and inter-subject variability, the lack of exhaustive ground-truth, the need for operator-dependent processing pipelines, and the highly non-linear vascular domain, still make the automatic inference of the cerebrovascular topology an open problem. In this thesis, brain vessels’ topology is inferred by focusing on their connectedness. With a novel framework, the brain vasculature is recovered from 3D angiographies by solving a connectivity-optimised anisotropic level-set over a voxel-wise tensor field representing the orientation of the underlying vasculature. Assuming vessels joining by minimal paths, a connectivity paradigm is formulated to automatically determine the vascular topology as an over-connected geodesic graph. Ultimately, deep-brain vascular structures are extracted with geodesic minimum spanning trees. The inferred topologies are then aligned with similar ones for labelling and propagating information over a non-linear vectorial domain, where the branching pattern of a set of vessels transcends a subject-specific quantized grid. Using a multi-source embedding of a vascular graph, the pairwise registration of topologies is performed with the state-of-the-art graph matching techniques employed in computer vision. Functional biomarkers are determined over the neurovascular graphs with two complementary approaches. Efficient approximations of blood flow and pressure drop account for autoregulation and compensation mechanisms in the whole network in presence of perturbations, using lumped-parameters analog-equivalents from clinical angiographies. Also, a localised NURBS-based parametrisation of bifurcations is introduced to model fluid-solid interactions by means of hemodynamic simulations using an isogeometric analysis framework, where both geometry and solution profile at the interface share the same homogeneous domain. Experimental results on synthetic and clinical angiographies validated the proposed formulations. Perspectives and future works are discussed for the group-wise alignment of cerebrovascular topologies over a population, towards defining cerebrovascular atlases, and for further topological optimisation strategies and risk prediction models for therapeutic inference. Most of the algorithms presented in this work are available as part of the open-source package VTrails

복부 CT에서 간과 혈관 분할 기법

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 신영길.복부 전산화 단층 촬영 (CT) 영상에서 정확한 간 및 혈관 분할은 체적 측정, 치료 계획 수립 및 추가적인 증강 현실 기반 수술 가이드와 같은 컴퓨터 진단 보조 시스템을 구축하는데 필수적인 요소이다. 최근 들어 컨볼루셔널 인공 신경망 (CNN) 형태의 딥 러닝이 많이 적용되면서 의료 영상 분할의 성능이 향상되고 있지만, 실제 임상에 적용할 수 있는 높은 일반화 성능을 제공하기는 여전히 어렵다. 또한 물체의 경계는 전통적으로 영상 분할에서 매우 중요한 요소로 이용되었지만, CT 영상에서 간의 불분명한 경계를 추출하기가 어렵기 때문에 현대 CNN에서는 이를 사용하지 않고 있다. 간 혈관 분할 작업의 경우, 복잡한 혈관 영상으로부터 학습 데이터를 만들기 어렵기 때문에 딥 러닝을 적용하기가 어렵다. 또한 얇은 혈관 부분의 영상 밝기 대비가 약하여 원본 영상에서 식별하기가 매우 어렵다. 본 논문에서는 위 언급한 문제들을 해결하기 위해 일반화 성능이 향상된 CNN과 얇은 혈관을 포함하는 복잡한 간 혈관을 정확하게 분할하는 알고리즘을 제안한다. 간 분할 작업에서 우수한 일반화 성능을 갖는 CNN을 구축하기 위해, 내부적으로 간 모양을 추정하는 부분이 포함된 자동 컨텍스트 알고리즘을 제안한다. 또한, CNN을 사용한 학습에 경계선의 개념이 새롭게 제안된다. 모호한 경계부가 포함되어 있어 전체 경계 영역을 CNN에 훈련하는 것은 매우 어렵기 때문에 반복되는 학습 과정에서 인공 신경망이 스스로 예측한 확률에서 부정확하게 추정된 부분적 경계만을 사용하여 인공 신경망을 학습한다. 실험적 결과를 통해 제안된 CNN이 다른 최신 기법들보다 정확도가 우수하다는 것을 보인다. 또한, 제안된 CNN의 일반화 성능을 검증하기 위해 다양한 실험을 수행한다. 간 혈관 분할에서는 간 내부의 관심 영역을 지정하기 위해 앞서 획득한 간 영역을 활용한다. 정확한 간 혈관 분할을 위해 혈관 후보 점들을 추출하여 사용하는 알고리즘을 제안한다. 확실한 후보 점들을 얻기 위해, 삼차원 영상의 차원을 먼저 최대 강도 투영 기법을 통해 이차원으로 낮춘다. 이차원 영상에서는 복잡한 혈관의 구조가 보다 단순화될 수 있다. 이어서, 이차원 영상에서 혈관 분할을 수행하고 혈관 픽셀들은 원래의 삼차원 공간상으로 역 투영된다. 마지막으로, 전체 혈관의 분할을 위해 원본 영상과 혈관 후보 점들을 모두 사용하는 새로운 레벨 셋 기반 알고리즘을 제안한다. 제안된 알고리즘은 복잡한 구조가 단순화되고 얇은 혈관이 더 잘 보이는 이차원 영상에서 얻은 후보 점들을 사용하기 때문에 얇은 혈관 분할에서 높은 정확도를 보인다. 실험적 결과에 의하면 제안된 알고리즘은 잘못된 영역의 추출 없이 다른 레벨 셋 기반 알고리즘들보다 우수한 성능을 보인다. 제안된 알고리즘은 간과 혈관을 분할하는 새로운 방법을 제시한다. 제안된 자동 컨텍스트 구조는 사람이 디자인한 학습 과정이 일반화 성능을 크게 향상할 수 있다는 것을 보인다. 그리고 제안된 경계선 학습 기법으로 CNN을 사용한 영상 분할의 성능을 향상할 수 있음을 내포한다. 간 혈관의 분할은 이차원 최대 강도 투영 기반 이미지로부터 획득된 혈관 후보 점들을 통해 얇은 혈관들이 성공적으로 분할될 수 있음을 보인다. 본 논문에서 제안된 알고리즘은 간의 해부학적 분석과 자동화된 컴퓨터 진단 보조 시스템을 구축하는 데 매우 중요한 기술이다.Accurate liver and its vessel segmentation on abdominal computed tomography (CT) images is one of the most important prerequisites for computer-aided diagnosis (CAD) systems such as volumetric measurement, treatment planning, and further augmented reality-based surgical guide. In recent years, the application of deep learning in the form of convolutional neural network (CNN) has improved the performance of medical image segmentation, but it is difficult to provide high generalization performance for the actual clinical practice. Furthermore, although the contour features are an important factor in the image segmentation problem, they are hard to be employed on CNN due to many unclear boundaries on the image. In case of a liver vessel segmentation, a deep learning approach is impractical because it is difficult to obtain training data from complex vessel images. Furthermore, thin vessels are hard to be identified in the original image due to weak intensity contrasts and noise. In this dissertation, a CNN with high generalization performance and a contour learning scheme is first proposed for liver segmentation. Secondly, a liver vessel segmentation algorithm is presented that accurately segments even thin vessels. To build a CNN with high generalization performance, the auto-context algorithm is employed. The auto-context algorithm goes through two pipelines: the first predicts the overall area of a liver and the second predicts the final liver using the first prediction as a prior. This process improves generalization performance because the network internally estimates shape-prior. In addition to the auto-context, a contour learning method is proposed that uses only sparse contours rather than the entire contour. Sparse contours are obtained and trained by using only the mispredicted part of the network's final prediction. Experimental studies show that the proposed network is superior in accuracy to other modern networks. Multiple N-fold tests are also performed to verify the generalization performance. An algorithm for accurate liver vessel segmentation is also proposed by introducing vessel candidate points. To obtain confident vessel candidates, the 3D image is first reduced to 2D through maximum intensity projection. Subsequently, vessel segmentation is performed from the 2D images and the segmented pixels are back-projected into the original 3D space. Finally, a new level set function is proposed that utilizes both the original image and vessel candidate points. The proposed algorithm can segment thin vessels with high accuracy by mainly using vessel candidate points. The reliability of the points can be higher through robust segmentation in the projected 2D images where complex structures are simplified and thin vessels are more visible. Experimental results show that the proposed algorithm is superior to other active contour models. The proposed algorithms present a new method of segmenting the liver and its vessels. The auto-context algorithm shows that a human-designed curriculum (i.e., shape-prior learning) can improve generalization performance. The proposed contour learning technique can increase the accuracy of a CNN for image segmentation by focusing on its failures, represented by sparse contours. The vessel segmentation shows that minor vessel branches can be successfully segmented through vessel candidate points obtained by reducing the image dimension. The algorithms presented in this dissertation can be employed for later analysis of liver anatomy that requires accurate segmentation techniques.Chapter 1 Introduction 1 1.1 Background and motivation 1 1.2 Problem statement 3 1.3 Main contributions 6 1.4 Contents and organization 9 Chapter 2 Related Works 10 2.1 Overview 10 2.2 Convolutional neural networks 11 2.2.1 Architectures of convolutional neural networks 11 2.2.2 Convolutional neural networks in medical image segmentation 21 2.3 Liver and vessel segmentation 37 2.3.1 Classical methods for liver segmentation 37 2.3.2 Vascular image segmentation 40 2.3.3 Active contour models 46 2.3.4 Vessel topology-based active contour model 54 2.4 Motivation 60 Chapter 3 Liver Segmentation via Auto-Context Neural Network with Self-Supervised Contour Attention 62 3.1 Overview 62 3.2 Single-pass auto-context neural network 65 3.2.1 Skip-attention module 66 3.2.2 V-transition module 69 3.2.3 Liver-prior inference and auto-context 70 3.2.4 Understanding the network 74 3.3 Self-supervising contour attention 75 3.4 Learning the network 81 3.4.1 Overall loss function 81 3.4.2 Data augmentation 81 3.5 Experimental Results 83 3.5.1 Overview 83 3.5.2 Data configurations and target of comparison 84 3.5.3 Evaluation metric 85 3.5.4 Accuracy evaluation 87 3.5.5 Ablation study 93 3.5.6 Performance of generalization 110 3.5.7 Results from ground-truth variations 114 3.6 Discussion 116 Chapter 4 Liver Vessel Segmentation via Active Contour Model with Dense Vessel Candidates 119 4.1 Overview 119 4.2 Dense vessel candidates 124 4.2.1 Maximum intensity slab images 125 4.2.2 Segmentation of 2D vessel candidates and back-projection 130 4.3 Clustering of dense vessel candidates 135 4.3.1 Virtual gradient-assisted regional ACM 136 4.3.2 Localized regional ACM 142 4.4 Experimental results 145 4.4.1 Overview 145 4.4.2 Data configurations and environment 146 4.4.3 2D segmentation 146 4.4.4 ACM comparisons 149 4.4.5 Evaluation of bifurcation points 154 4.4.6 Computational performance 159 4.4.7 Ablation study 160 4.4.8 Parameter study 162 4.5 Application to portal vein analysis 164 4.6 Discussion 168 Chapter 5 Conclusion and Future Works 170 Bibliography 172 초록 197Docto

Brain vasculature segmentation from magnetic resonance angiographic image

Master'sMASTER OF ENGINEERIN

Characterization and processing of novel neck photoplethysmography signals for cardiorespiratory monitoring

Epilepsy is a neurological disorder causing serious brain seizures that severely affect the patients' quality of life. Sudden unexpected death in epilepsy (SUDEP), for which no evident decease reason is found after post-mortem examination, is a common cause of mortality. The mechanisms leading to SUDEP are uncertain, but, centrally mediated apneic respiratory dysfunction, inducing dangerous hypoxemia, plays a key role. Continuous physiological monitoring appears as the only reliable solution for SUDEP prevention. However, current seizure-detection systems do not show enough sensitivity and present a high number of intolerable false alarms. A wearable system capable of measuring several physiological signals from the same body location, could efficiently overcome these limitations. In this framework, a neck wearable apnea detection device (WADD), sensing airflow through tracheal sounds, was designed. Despite the promising performance, it is still necessary to integrate an oximeter sensor into the system, to measure oxygen saturation in blood (SpO2) from neck photoplethysmography (PPG) signals, and hence, support the apnea detection decision. The neck is a novel PPG measurement site that has not yet been thoroughly explored, due to numerous challenges. This research work aims to characterize neck PPG signals, in order to fully exploit this alternative pulse oximetry location, for precise cardiorespiratory biomarkers monitoring. In this thesis, neck PPG signals were recorded, for the first time in literature, in a series of experiments under different artifacts and respiratory conditions. Morphological and spectral characteristics were analyzed in order to identify potential singularities of the signals. The most common neck PPG artifacts critically corrupting the signal quality, and other breathing states of interest, were thoroughly characterized in terms of the most discriminative features. An algorithm was further developed to differentiate artifacts from clean PPG signals. Both, the proposed characterization and classification model can be useful tools for researchers to denoise neck PPG signals and exploit them in a variety of clinical contexts. In addition to that, it was demonstrated that the neck also offered the possibility, unlike other body parts, to extract the Jugular Venous Pulse (JVP) non-invasively. Overall, the thesis showed how the neck could be an optimum location for multi-modal monitoring in the context of diseases affecting respiration, since it not only allows the sensing of airflow related signals, but also, the breathing frequency component of the PPG appeared more prominent than in the standard finger location. In this context, this property enabled the extraction of relevant features to develop a promising algorithm for apnea detection in near-real time. These findings could be of great importance for SUDEP prevention, facilitating the investigation of the mechanisms and risk factors associated to it, and ultimately reduce epilepsy mortality.Open Acces

Specular reflection removal and bloodless vessel segmentation for 3-D heart model reconstruction from single view images

Three Dimensional (3D) human heart model is attracting attention for its role in medical images for education and clinical purposes. Analysing 2D images to obtain meaningful information requires a certain level of expertise. Moreover, it is time consuming and requires special devices to obtain aforementioned images. In contrary, a 3D model conveys much more information. 3D human heart model reconstruction from medical imaging devices requires several input images, while reconstruction from a single view image is challenging due to the colour property of the heart image, light reflections, and its featureless surface. Lights and illumination condition of the operating room cause specular reflections on the wet heart surface that result in noises forming of the reconstruction process. Image-based technique is used for the proposed human heart surface reconstruction. It is important the reflection is eliminated to allow for proper 3D reconstruction and avoid imperfect final output. Specular reflections detection and correction process examine the surface properties. This was implemented as a first step to detect reflections using the standard deviation of RGB colour channel and the maximum value of blue channel to establish colour, devoid of specularities. The result shows the accurate and efficient performance of the specularities removing process with 88.7% similarity with the ground truth. Realistic 3D heart model reconstruction was developed based on extraction of pixel information from digital images to allow novice surgeons to reduce the time for cardiac surgery training and enhancing their perception of the Operating Theatre (OT). Cardiac medical imaging devices such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) images, or Echocardiography provide cardiac information. However,these images from medical modalities are not adequate, to precisely simulate the real environment and to be used in the training simulator for cardiac surgery. The propose method exploits and develops techniques based on analysing real coloured images taken during cardiac surgery in order to obtain meaningful information of the heart anatomical structures. Another issue is the different human heart surface vessels. The most important vessel region is the bloodless, lack of blood, vessels. Surgeon faces some difficulties in locating the bloodless vessel region during surgery. The thesis suggests a technique of identifying the vessels’ Region of Interest (ROI) to avoid surgical injuries by examining an enhanced input image. The proposed method locates vessels’ ROI by using Decorrelation Stretch technique. This Decorrelation Stretch can clearly enhance the heart’s surface image. Through this enhancement, the surgeon become enables effectively identifying the vessels ROI to perform the surgery from textured and coloured surface images. In addition, after enhancement and segmentation of the vessels ROI, a 3D reconstruction of this ROI takes place and then visualize it over the 3D heart model. Experiments for each phase in the research framework were qualitatively and quantitatively evaluated. Two hundred and thirteen real human heart images are the dataset collected during cardiac surgery using a digital camera. The experimental results of the proposed methods were compared with manual hand-labelling ground truth data. The cost reduction of false positive and false negative of specular detection and correction processes of the proposed method was less than 24% compared to other methods. In addition, the efficient results of Root Mean Square Error (RMSE) to measure the correctness of the z-axis values to reconstruction of the 3D model accurately compared to other method. Finally, the 94.42% accuracy rate of the proposed vessels segmentation method using RGB colour space achieved is comparable to other colour spaces. Experimental results show that there is significant efficiency and robustness compared to existing state of the art methods

Temporal registration of vessels in retinal images

Master'sMASTER OF SCIENC