Raking the Cocktail Party

We present the concept of an acoustic rake receiver---a microphone beamformer that uses echoes to improve the noise and interference suppression. The rake idea is well-known in wireless communications; it involves constructively combining different multipath components that arrive at the receiver antennas. Unlike spread-spectrum signals used in wireless communications, speech signals are not orthogonal to their shifts. Therefore, we focus on the spatial structure, rather than temporal. Instead of explicitly estimating the channel, we create correspondences between early echoes in time and image sources in space. These multiple sources of the desired and the interfering signal offer additional spatial diversity that we can exploit in the beamformer design. We present several "intuitive" and optimal formulations of acoustic rake receivers, and show theoretically and numerically that the rake formulation of the maximum signal-to-interference-and-noise beamformer offers significant performance boosts in terms of noise and interference suppression. Beyond signal-to-noise ratio, we observe gains in terms of the \emph{perceptual evaluation of speech quality} (PESQ) metric for the speech quality. We accompany the paper by the complete simulation and processing chain written in Python. The code and the sound samples are available online at \url{http://lcav.github.io/AcousticRakeReceiver/}.Comment: 12 pages, 11 figures, Accepted for publication in IEEE Journal on Selected Topics in Signal Processing (Special Issue on Spatial Audio

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

Mapping and Localization in Urban Environments Using Cameras

In this work we present a system to fully automatically create a highly accurate visual feature map from image data aquired from within a moving vehicle. Moreover, a system for high precision self localization is presented. Furthermore, we present a method to automatically learn a visual descriptor. The map relative self localization is centimeter accurate and allows autonomous driving

A bounded degree SOS hierarchy for polynomial optimization

We consider a new hierarchy of semidefinite relaxations for the general polynomial optimization problem $(P):\:f^{\ast}=\min \{\,f(x):x\in K\,\}$ on a compact basic semi-algebraic set $K\subset\R^n$. This hierarchy combines some advantages of the standard LP-relaxations associated with Krivine's positivity certificate and some advantages of the standard SOS-hierarchy. In particular it has the following attractive features: (a) In contrast to the standard SOS-hierarchy, for each relaxation in the hierarchy, the size of the matrix associated with the semidefinite constraint is the same and fixed in advance by the user. (b) In contrast to the LP-hierarchy, finite convergence occurs at the first step of the hierarchy for an important class of convex problems. Finally (c) some important techniques related to the use of point evaluations for declaring a polynomial to be zero and to the use of rank-one matrices make an efficient implementation possible. Preliminary results on a sample of non convex problems are encouraging

Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging

Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy. Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information. In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems

Volumetric MRI Reconstruction from 2D Slices in the Presence of Motion

Despite recent advances in acquisition techniques and reconstruction algorithms, magnetic resonance imaging (MRI) remains challenging in the presence of motion. To mitigate this, ultra-fast two-dimensional (2D) MRI sequences are often used in clinical practice to acquire thick, low-resolution (LR) 2D slices to reduce in-plane motion. The resulting stacks of thick 2D slices typically provide high-quality visualizations when viewed in the in-plane direction. However, the low spatial resolution in the through-plane direction in combination with motion commonly occurring between individual slice acquisitions gives rise to stacks with overall limited geometric integrity. In further consequence, an accurate and reliable diagnosis may be compromised when using such motion-corrupted, thick-slice MRI data. This thesis presents methods to volumetrically reconstruct geometrically consistent, high-resolution (HR) three-dimensional (3D) images from motion-corrupted, possibly sparse, low-resolution 2D MR slices. It focuses on volumetric reconstructions techniques using inverse problem formulations applicable to a broad field of clinical applications in which associated motion patterns are inherently different, but the use of thick-slice MR data is current clinical practice. In particular, volumetric reconstruction frameworks are developed based on slice-to-volume registration with inter-slice transformation regularization and robust, complete-outlier rejection for the reconstruction step that can either avoid or efficiently deal with potential slice-misregistrations. Additionally, this thesis describes efficient Forward-Backward Splitting schemes for image registration for any combination of differentiable (not necessarily convex) similarity measure and convex (not necessarily smooth) regularization with a tractable proximal operator. Experiments are performed on fetal and upper abdominal MRI, and on historical, printed brain MR films associated with a uniquely long-term study dating back to the 1980s. The results demonstrate the broad applicability of the presented frameworks to achieve robust reconstructions with the potential to improve disease diagnosis and patient management in clinical practice