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

Proper shape representation of single figure and multi-figure anatomical objects

Extracting anatomic objects from medical images is an important process in various medical applications. This extraction, called image segmentation, is often realized by deformable models. Among deformable model methods, medial deformable models have the unique advantage of representing not only the object boundary surfaces but also the object interior volume. Based on one medial deformable model called the m-rep, the main goal of this dissertation is to provide proper shape representations of simple anatomical objects of one part and complex anatomical objects of multiple parts in a population. This dissertation focuses on several challenges in the existing medially based deformable model method: 1. how to derive a proper continuous form by interpolating a discrete medial shape representation; 2. how to represent complex objects with several parts and do statistical analysis on them; 3. how to avoid local shape defects, such as folding or creasing, in shapes represented by the deformable model. The proposed methods in this dissertation address these challenges in more detail: 1. An interpolation method for a discrete medial shape model is proposed to guarantee the legality of the interpolated shape. This method is based on the integration of medial shape operators. 2. A medially based representation with hierarchy is proposed to represent complex objects with multiple parts by explicitly modeling interrelations between object parts and modeling smooth transitions between each pair of connected parts. A hierarchical statistical analysis is also proposed for these complex objects. 3. A method to fit a medial model to binary images is proposed to use an explicit legality penalty derived from the medial shape operators. Probability distributions learned from the fitted shape models by the proposed fitting method have proven to yield better image segmentation results

Neighbor-constrained segmentation with 3d deformable models

Abstract. A novel method for the segmentation of multiple objects from 3D medical images using inter-object constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a Maximum A Posteriori(MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring positions and shapes of a set of training images. By estimating the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. We feel that this method is useful in situations where there is limited inter-object information as opposed to robust global atlases. Results and validation from various experiments on synthetic data and medical imagery in 2D and 3D are demonstrated.

Curve sampling and geometric conditional simulation

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 195-203).The main objective of this thesis is the development and exploitation of techniques to generate geometric samples for the purpose of image segmentation. A sampling-based approach provides a number of benefits over existing optimization-based methods such as robustness to noise and model error, characterization of segmentation uncertainty, natural handling of multi-modal distributions, and incorporation of partial segmentation information. This is important for applications which suffer from, e.g., low signal-to-noise ratio (SNR) or ill-posedness. We create a curve sampling algorithm using the Metropolis-Hastings Markov chain Monte Carlo (MCMC) framework. With this method, samples from a target distribution [pi] (which can be evaluated but not sampled from directly) are generated by creating a Markov chain whose stationary distribution is [pi] and sampling many times from a proposal distribution q. We define a proposal distribution using random Gaussian curve perturbations, and show how to ensure detailed balance and ergodicity of the chain so that iterates of the Markov chain asymptotically converge to samples from [pi]. We visualize the resulting samples using techniques such as confidence bounds and principal modes of variation and demonstrate the algorithm on examples such as prostate magnetic resonance (MR) images, brain MR images, and geological structure estimation using surface gravity observations. We generalize our basic curve sampling framework to perform conditional simulation: a portion of the solution space is specified, and the remainder is sampled conditioned on that information. For difficult segmentation problems which are currently done manually by human experts, reliable semi-automatic segmentation approaches can significantly reduce the amount of time and effort expended on a problem. We also extend our framework to 3D by creating a hybrid 2D/3D Markov chain surface model.For this approach, the nodes on the chain represent entire curves on parallel planes,and the slices combine to form a complete surface. Interaction among the curves is described by an undirected Markov chain, and we describe methods to sample from this model using both local Metropolis-Hastings methods and the embedded hidden Markov model (HMM) algorithm.by Ayres C. Fan.Ph.D