Efficient extraction of semantic information from medical images in large datasets using random forests

Large datasets of unlabelled medical images are increasingly becoming available; however only a small subset tend to be manually semantically labelled as it is a tedious and extremely time-consuming task to do for large datasets. This thesis aims to tackle the problem of efficiently extracting semantic information in the form of image segmentations and organ localisations from large datasets of unlabelled medical images. To do so, we investigate the suitability of supervoxels and random classification forests for the task. The first contribution of this thesis is a novel method for efficiently estimating coarse correspondences between pairs of images that can handle difficult cases that exhibit large variations in fields of view. The proposed methods adapts the random forest framework, which is a supervised learning algorithm, to work in an unsupervised manner by automatically generating labels for training via the use of supervoxels. The second contribution of this thesis is a method that extends our first contribution so as to be applicable efficiently on a large dataset of images. The proposed method is efficient and can be used to obtain correspondences between a large number of object-like supervoxels that are representative of organ structures in the images. The method is evaluated for the applications of organ-based image retrieval and weakly-supervised image segmentation using extremely minimal user input. While the method does not achieve image segmentation accuracies for all organs in an abdominal CT dataset compared to current fully-supervised state-of-the-art methods, it does provide a promising way for efficiently extracting and parsing a large dataset of medical images for the purpose of further processing.Open Acces

Combining Shape and Learning for Medical Image Analysis

Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today\u27s automatic methods succeed to meet these requirements.\ua0The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields

Semantic Instance Annotation of Street Scenes by 3D to 2D Label Transfer

Semantic annotations are vital for training models for object recognition, semantic segmentation or scene understanding. Unfortunately, pixelwise annotation of images at very large scale is labor-intensive and only little labeled data is available, particularly at instance level and for street scenes. In this paper, we propose to tackle this problem by lifting the semantic instance labeling task from 2D into 3D. Given reconstructions from stereo or laser data, we annotate static 3D scene elements with rough bounding primitives and develop a model which transfers this information into the image domain. We leverage our method to obtain 2D labels for a novel suburban video dataset which we have collected, resulting in 400k semantic and instance image annotations. A comparison of our method to state-of-the-art label transfer baselines reveals that 3D information enables more efficient annotation while at the same time resulting in improved accuracy and time-coherent labels.Comment: 10 pages in Conference on Computer Vision and Pattern Recognition (CVPR), 201

Efficient multi-level scene understanding in videos

Automatic video parsing is a key step towards human-level dynamic scene understanding, and a fundamental problem in computer vision. A core issue in video understanding is to infer multiple scene properties of a video in an efficient and consistent manner. This thesis addresses the problem of holistic scene understanding from monocular videos, which jointly reason about semantic and geometric scene properties from multiple levels, including pixelwise annotation of video frames, object instance segmentation in spatio-temporal domain, and/or scene-level description in terms of scene categories and layouts. We focus on four main issues in the holistic video understanding: 1) what is the representation for consistent semantic and geometric parsing of videos? 2) how do we integrate high-level reasoning (e.g., objects) with pixel-wise video parsing? 3) how can we do efficient inference for multi-level video understanding? and 4) what is the representation learning strategy for efficient/cost-aware scene parsing? We discuss three multi-level video scene segmentation scenarios based on different aspects of scene properties and efficiency requirements. The first case addresses the problem of consistent geometric and semantic video segmentation for outdoor scenes. We propose a geometric scene layout representation, or a stage scene model, to efficiently capture the dependency between the semantic and geometric labels. We build a unified conditional random field for joint modeling of the semantic class, geometric label and the stage representation, and design an alternating inference algorithm to minimize the resulting energy function. The second case focuses on the problem of simultaneous pixel-level and object-level segmentation in videos. We propose to incorporate foreground object information into pixel labeling by jointly reasoning semantic labels of supervoxels, object instance tracks and geometric relations between objects. In order to model objects, we take an exemplar approach based on a small set of object annotations to generate a set of object proposals. We then design a conditional random field framework that jointly models the supervoxel labels and object instance segments. To scale up our method, we develop an active inference strategy to improve the efficiency of multi-level video parsing, which adaptively selects an informative subset of object proposals and performs inference on the resulting compact model. The last case explores the problem of learning a flexible representation for efficient scene labeling. We propose a dynamic hierarchical model that allows us to achieve flexible trade-offs between efficiency and accuracy. Our approach incorporates the cost of feature computation and model inference, and optimizes the model performance for any given test-time budget. We evaluate all our methods on several publicly available video and image semantic segmentation datasets, and demonstrate superior performance in efficiency and accuracy. Keywords: Semantic video segmentation, Multi-level scene understanding, Efficient inference, Cost-aware scene parsin

Visual Perception For Robotic Spatial Understanding

Humans understand the world through vision without much effort. We perceive the structure, objects, and people in the environment and pay little direct attention to most of it, until it becomes useful. Intelligent systems, especially mobile robots, have no such biologically engineered vision mechanism to take for granted. In contrast, we must devise algorithmic methods of taking raw sensor data and converting it to something useful very quickly. Vision is such a necessary part of building a robot or any intelligent system that is meant to interact with the world that it is somewhat surprising we don\u27t have off-the-shelf libraries for this capability. Why is this? The simple answer is that the problem is extremely difficult. There has been progress, but the current state of the art is impressive and depressing at the same time. We now have neural networks that can recognize many objects in 2D images, in some cases performing better than a human. Some algorithms can also provide bounding boxes or pixel-level masks to localize the object. We have visual odometry and mapping algorithms that can build reasonably detailed maps over long distances with the right hardware and conditions. On the other hand, we have robots with many sensors and no efficient way to compute their relative extrinsic poses for integrating the data in a single frame. The same networks that produce good object segmentations and labels in a controlled benchmark still miss obvious objects in the real world and have no mechanism for learning on the fly while the robot is exploring. Finally, while we can detect pose for very specific objects, we don\u27t yet have a mechanism that detects pose that generalizes well over categories or that can describe new objects efficiently. We contribute algorithms in four of the areas mentioned above. First, we describe a practical and effective system for calibrating many sensors on a robot with up to 3 different modalities. Second, we present our approach to visual odometry and mapping that exploits the unique capabilities of RGB-D sensors to efficiently build detailed representations of an environment. Third, we describe a 3-D over-segmentation technique that utilizes the models and ego-motion output in the previous step to generate temporally consistent segmentations with camera motion. Finally, we develop a synthesized dataset of chair objects with part labels and investigate the influence of parts on RGB-D based object pose recognition using a novel network architecture we call PartNet

Three-Dimensional Object Search, Understanding, and Pose Estimation with Low-Cost Sensors

With the recent development of low-cost depth sensors, an entirely new type of 3D data is being generated rapidly by regular consumers. Traditionally, 3D data is produced by a small number of professional designers (i.e., the Computer Aided Design (CAD) model); however, 3D data from massive consumer-level sensors has the potential of introducing many new applications, such as user-captured 3D warehouse and search engines, robots with 3D sensing capability, and customized 3D printing. Nevertheless, the low-cost sensors used by general consumers also pose new technological challenges. First, they have relatively high levels of sensor noise. Second, the use of such consumer devices is often in uncontrolled settings, resulting in challenging conditions, such as poor lighting, cluttered scenes, and object occlusion. To address such emerging opportunities and associated challenges, this dissertation is dedicated to the development of novel algorithms and systems for 3D data understanding and processing, using input from a consumer-level 3D sensor. In particular, the key problems of 3D shape retrieval, scene understanding, and pose recognition are explored in order to present a comprehensive coverage of the key aspects of content-based 3D shape analysis. To resolve the aforementioned challenges, we propose a flexible Markov Random Field (MRF) framework that uses local information to allow partial matching, and thus address the model incompleteness problem; the framework also uses higher-order correlation to provide additional robustness against sensor noise. With the MRF framework, these 3D analysis problems can be transformed into a unified potential energy minimization problem, while preserving the flexibility to adapt to different settings and resolve the unique challenges of each problem. The contributions of the dissertation include: a. Cross-Domain 3D Retrieval: First we tackle the problem of searching 3D noise- free models using noisy data captured by low-cost 3D sensors – a unique cross-domain setting. To manage the challenges of sensor noise and model incompleteness from consumer-level sensors, we propose a novel MRF formulation for the retrieval problem. The potential function of the random field is designed to capture both the local shape and global spatial consistency in order to preserve the local matching capability, while offering robustness against the sensor noise. The specific form of the potential functions is determined efficiently by a series of weak classifiers, thus forming a variant of the Regression Tree Field (RTF). We achieve better retrieval precision and recall in the cross-domain settings with a consumer-level depth sensor compared with state-of-the-art approaches. b. 3D Scene Understanding: We develop a scene understanding system based on input from consumer-level depth sensors. To resolve the key challenge of the lack of annotated 3D training data, we construct an MRF that connects the input 3D point cloud and the associated 2D reference images, based on which the 3D point cloud is stitched. A series of weak classifiers are trained to obtain an approximate semantic segmentation result from the reference images. The potential function of the field is designed to integrate the results from the classifiers, while taking advantage of the 3D spatial consistency in order to output a comprehensive scene understanding result. We achieve comparable accuracy and much faster speed compared with state-of-the-art 3D scene understanding systems, with the difference that we do not require annotated 3D training data. c. Pose Recognition of Deformable Objects: We develop a method for supporting a robotics system to recognize pose and manipulate deformable objects. More specifically, garment pose is recognized with the help of an offline simulated database and the proposed retrieval approach. We use a novel binary feature representation extracted from the reconstructed 3D surfaces in order to allow efficient matching, thus achieving real-time performance. A spatial weight is further learned in order to integrate the local matching result. The system shows superior recognition accuracy and faster speed than the state-of-the-art approaches. d. Application with 2D Data: In addition to the traditional 3D applications, we explore the possibility of extending MRF formulation to 2D data, especially those used in classical low-level 2D vision problems, such as image deblurring and denoising. One well-known technique that uses image prior, the probabilistic patched-based prior, is known to have bottlenecks in finding the most similar model from a model set, which can be posed as a retrieval problem. Therefore, we apply the MRF formulation originally developed for 3D shape retrieval, and extend it to this 2D problem by introducing a grid-like random field structure. We can achieve 40x acceleration compared with the state-of-the-art algorithm, while preserving quality. We organize the dissertation as follows. First, the core problems of 3D shape retrieval, scene understanding, and pose recognition, and with the proposed solutions that use MRF and RTF are explored in Part I. In Part II, the extension to 2D data is discussed. Extensive evaluation is performed in each specific task in order to compare the proposed approaches with state-of-the-art algorithms and systems, and also to justify the components of the proposed methods. Finally, in Part III, we include the conclusion remarks and discussion of open issues and future work