Multiatlas segmentation as nonparametric regression

pre-printThis paper proposes a novel theoretical framework to model and analyze the statistical characteristics of a wide range of segmentation methods that incorporate a database of label maps or atlases; such methods are termed as label fusion or multiatlas segmentation.We model these multiatlas segmentation problems as nonparametric regression problems in the high-dimensional space of image patches. We analyze the nonparametric estimator's convergence behavior that characterizes expected segmentation error as a function of the size of the multiatlas database. We show that this error has an analytic form involving several parameters that are fundamental to the specific segmentation problem (determined by the chosen anatomical structure, imaging modality, registration algorithm, and label-fusion algorithm). We describe how to estimate these parameters and show that several human anatomical structures exhibit the trends modeled analytically. We use these parameter estimates to optimize the regression estimator.We show that the expected error for large database sizes is well predicted by models learned on small databases. Thus, a few expert segmentations can help predict the database sizes required to keep the expected error below a specified tolerance level. Such cost-benefit analysis is crucial for deploying clinical multiatlas segmentation systems

Patch-based segmentation with spatial context for medical image analysis

Accurate segmentations in medical imaging form a crucial role in many applications from pa- tient diagnosis to population studies. As the amount of data generated from medical images increases, the ability to perform this task without human intervention becomes ever more de- sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from images which have already been manually labelled by clinical experts. Methods using this ap- proach have been shown to be e ective in many applications, demonstrating great potential for automatic labelling of large datasets. However, these methods usually require the use of image registration and are dependent on the outcome of the registration. Any registrations errors that occur are also propagated to the segmentation process and are likely to have an adverse e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a relaxation of the required image alignment, whilst achieving similar results. In general, these methods label each voxel of a target image by comparing the image patch centred on the voxel with neighbouring patches from an atlas library and assigning the most likely label according to the closest matches. The main contributions of this thesis focuses around this approach in providing accurate segmentation results whilst minimising the dependency on registration quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework, which utilises both intensity and spatial information, and explore the use of spatial context in a diverse range of applications. The proposed methods extend the potential for patch-based segmentation to tolerate registration errors by rede ning the \locality" for patch selection and comparison, whilst also allowing similar looking patches from di erent anatomical structures to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging from the brain to the knees, demonstrating its potential with results which are competitive to state-of-the-art techniques.Open Acces

Semi-supervised segmentation of ultrasound images based on patch representation and continuous min cut.

Ultrasound segmentation is a challenging problem due to the inherent speckle and some artifacts like shadows, attenuation and signal dropout. Existing methods need to include strong priors like shape priors or analytical intensity models to succeed in the segmentation. However, such priors tend to limit these methods to a specific target or imaging settings, and they are not always applicable to pathological cases. This work introduces a semi-supervised segmentation framework for ultrasound imaging that alleviates the limitation of fully automatic segmentation, that is, it is applicable to any kind of target and imaging settings. Our methodology uses a graph of image patches to represent the ultrasound image and user-assisted initialization with labels, which acts as soft priors. The segmentation problem is formulated as a continuous minimum cut problem and solved with an efficient optimization algorithm. We validate our segmentation framework on clinical ultrasound imaging (prostate, fetus, and tumors of the liver and eye). We obtain high similarity agreement with the ground truth provided by medical expert delineations in all applications (94% DICE values in average) and the proposed algorithm performs favorably with the literature

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