Davatzikos, Christos
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Publication Optimized biopsy procedures for estimating Gleason Score and prostate cancer volume(2008-09-01) Ou, Yangming; Shen, Dinggang; Fan, Yong; Davatzikos, Christos; Zeng, Jianchao; Sun, LeonProstate biopsy is the gold standard procedure for pre-operatively estimating Gleason Score (GS) and cancer volume (CV), which are two important surrogate markers for prostate cancer aggressiveness. Currently, biopsy estimates GS based on architectural patterns of the sampled tissue at the microscopic level [1] and estimates CV mostly based on the percent positive biopsies. However, underestimations are sometimes observed mainly due to the sampling errors of biopsy [2-5]. This problem is partially alleviated in this paper, where we have developed optimized biopsy procedures that could differentiate between prostate specimens having high and low GS/CV by sampling the spatial cancer distributions at the macro level. Differentiation rates of 81.93% (for GS) and 94.79% (for CV) have been obtained under cross validation in a population of prostatectomy specimens. To the best of our knowledge, the optimized biopsy procedures are the first ones that use (macro-level) spatial cancer distributions to estimate GS and CV. More validations might be needed to reveal its generalization ability.Publication Sampling the spatial patterns of cancer: Optimized biopsy procedures for estimating prostate cancer volume and Gleason Score(2009-08-01) Ou, Yangming; Shen, Dinggang; Zeng, Jianchao; Davatzikos, Christos; Sun, Leon; Moul, JuddProstate biopsy is the current gold-standard procedure for prostate cancer diagnosis. Existing prostate biopsy procedures have been mostly focusing on detecting cancer presence. However, they often ignore the potential use of biopsy to estimate cancer volume (CV) and Gleason Score (GS, a cancer grade descriptor), the two surrogate markers for cancer aggressiveness and the two crucial factors for treatment planning. To fill up this vacancy, this paper assumes and demonstrates that, by optimally sampling the spatial patterns of cancer, biopsy procedures can be specifically designed for estimating CV and GS. Our approach combines image analysis and machine learning tools in an atlas-based population study that consists of three steps. First, the spatial distributions of cancer in a patient population are learned, by constructing statistical atlases from histological images of prostate specimens with known cancer ground truths. Then, the optimal biopsy locations are determined in a feature selection formulation, so that biopsy outcomes (either cancer presence or absence) at those locations could be used to differentiate, at the best rate, between the existing specimens having different (high vs. low) CV/GS values. Finally, the optimized biopsy locations are utilized to estimate whether a new-coming prostate cancer patient has high or low CV/GS values, based on a binary classification formulation. The estimation accuracy and the generalization ability are evaluated by the classification rates and the associated receiver-operating-characteristic (ROC) curves in cross validations. The optimized biopsy procedures are also designed to be robust to the almost inevitable needle displacement errors in clinical practice, and are found to be robust to variations in the optimization parameters as well as the training populations.Publication Probabilistic Segmentation of Brain Tumors Based on Multi-Modality Magnetic Resonance Images(2007-04-01) Verma, Ragini; Ou, Yangming; Cai, Hongmin; Melhem, Elias R.; Davatzikos, Christos; Lee, Seung-KooIn this paper, multi-modal Magnetic Resonance (MR) images are integrated into a tissue profile that aims at differentiating tumor components, edema and normal tissue. This is achieved by a tissue classification technique that learns the appearance models of different tissue types based on training samples identified by an expert and assigns tissue labels to each voxel. These tissue classifiers produce probabilistic tissue maps reflecting imaging characteristics of tumors and surrounding tissues that may be employed to aid in diagnosis, tumor boundary delineation, surgery and treatment planning. The main contributions of this work are: 1) conventional structural MR modalities are combined with diffusion tensor imaging data to create an integrated multimodality profile for brain tumors, and 2) in addition to the tumor components of enhancing and non-enhancing tumor types, edema is also characterized as a separate class in our framework. Classification performance is tested on 22 diverse tumor cases using cross-validation.Publication Detecting Mutually-Salient Landmark Pairs with MRF Regularization(2010-04-01) Ou, Yangming; Bilello, Michel; Mansour, Mohamed; Davatzikos, Christos; Besbes, Ahmed; Paragios, NikosIn this paper, we present a framework for extracting mutually-salient landmark pairs for registration. Traditional methods detect landmarks one-by-one and separately in two images. Therefore, the detected landmarks might inherit low discriminability and are not necessarily good for matching. In contrast, our method detects landmarks pair-by-pair across images, and those pairs are required to be mutually-salient, i.e., uniquely corresponding to each other. The second merit of our framework is that, instead of finding individually optimal correspondence, which is a local approach and could cause self-intersection of the resultant deformation, our framework adopts a Markov-random-field (MRF)-based spatial arrangement to select the globally optimal landmark pairs. In this way, the geometric consistency of the correspondences is maintained and the resultant deformations are relatively smooth and topology-preserving. Promising experimental validation through a radiologist’s evaluation of the established correspondences is presented.Publication Classifying spatial patterns of brain activity with machine learning methods: application to lie detection(2005-11-15) Davatzikos, Christos; Ruparel, Kosha; Fan, Yong; Shen, Dinggang; Gur, Ruben; Langleben, Daniel D.; Acharyya, M.; Loughead, JamesPatterns of brain activity during deception have recently been characterized with fMRI on the multi-subject average group level. The clinical value of fMRI in lie detection will be determined by the ability to detect deception in individual subjects, rather than group averages. High-dimensional non-linear pattern classification methods applied to functional magnetic resonance (fMRI) images were used to discriminate between the spatial patterns of brain activity associated with lie and truth. In 22 participants performing a forced-choice deception task, 99% of the true and false responses were discriminated correctly. Predictive accuracy, assessed by cross-validation in participants not included in training, was 88%. The results demonstrate the potential of non-linear machine learning techniques in lie detection and other possible clinical applications of fMRI in individual subjects, and indicate that accurate clinical tests could be based on measurements of brain function with fMRI.Publication Statistically-Constrained High-Dimensional Warping Using Wavelet-Based Priors(2006-06-01) Zue, Zhong; Shen, Dinggang; Davatzikos, ChristosIn this paper, a Statistical Model of Deformation (SMD) that captures the statistical prior distribution of high-dimensional deformations more accurately and effectively than conventional PCA-based statistical shape models is used to regularize deformable registration. SMD utilizes a wavelet-based representation of statistical variation of a deformation field and its Jacobian, and it is able to capture both global and fine shape detail without overconstraining the deformation process. This approach is shown to produce more accurate and robust registration results in MR brain images, relative to the registration methods that use Laplacian-based smoothness constraints of deformation fields. In experiments, we evaluate the SMD-constrained registration by comparing the performance of registration with and without SMD in a specific deformable registration framework. The proposed method can potentially incorporate various registration algorithms to improve their robustness and stability using statistically-based regularization.Publication Characterizing and Analyzing Diffusion Tensor Images by Learning their Underlying Manifold Structure(2006-01-01) Khurd, Parmeshwar; Verma, Ragini; Davatzikos, ChristosThe growing importance of diffusion tensor imaging (DTI) in studying the white matter architecture in normal and pathologic states necessitates the development of tools for comprehensive analysis of diffusion tensor data. Operations such as multivariate statistical analysis and hypothesis testing, interpolation and filtering, must now be performed on tensor data, and must overcome challenges introduced by the non-linearity and high dimensionality of the tensors. In this paper, we present a novel approach to performing these computations by modeling the underlying manifold structure of the tensors, using a combination of two manifold learning techniques, isometric mapping (ISOMAP) and local tangent space alignment (LTSA). While ISOMAP identifies the dimensionality of the manifold of the tensors and embeds the tensors into a linear space, facilitating statistical computations therein, operations like interpolation and filtering, integral to the process of normalization, require the reconstruction of the tensor in the tensor domain. To obtain this reverse mapping from the linear space to the tensor domain, i.e. to the domain of the original tensor data, we use LTSA. The modeling of the underlying manifold structure renders our approach better applicable to tensor data than existing methods that may not always be able to capture the non-linearity present in the tensors under consideration. In various simulations with known ground truth, we demonstrate the effectiveness of our framework based on ISOMAP and LTSA in performing a comprehensive analysis of DTI data.Publication A Comparative Study of Biomechanical Simulators in Deformable Registration of Brain Tumor Images(2008-03-01) Zacharaki, Evangelia; Biros, George; Davatzikos, Christos; Hogea, Cosmina SSimulating the brain tissue deformation caused by tumor growth has been found to aid the deformable registration of brain tumor images. In this paper, we evaluate the impact that different biomechanical simulators have on the accuracy of deformable registration. We use two alternative frameworks for biomechanical simulations of mass effect in 3-D magnetic resonance (MR) brain images. The first one is based on a finite-element model of nonlinear elasticity and unstructured meshes using the commercial software package ABAQUS. The second one employs incremental linear elasticity and regular grids in a fictitious domain method. In practice, biomechanical simulations via the second approach may be at least ten times faster. Landmarks error and visual examination of the coregistered images indicate that the two alternative frameworks for biomechanical simulations lead to comparable results of deformable registration. Thus, the computationally less expensive biomechanical simulator offers a practical alternative for registration purposes.Publication Registering Histological and MR Images of Prostate for Image-based Cancer Detection(2007-11-01) Ou, Yangming; Feldman, Michael; Tomaszewski, John; Davatzikos, Christos; Zhan, Yiqiang; Shen, DinggangRationale and Objectives Needle biopsy is currently the only way to confirm prostate cancer. To increase prostate cancer diagnostic rate, needles are expected to be deployed at suspicious cancer locations. High contrast MR imaging provides a powerful tool for detecting suspicious cancerous tissues. To do this, MR appearances of cancerous tissue should be characterized and learned from a sufficient number of prostate MR images with known cancer information. However, ground-truth cancer information is only available in histological images. Therefore, it is necessary to warp ground-truth cancerous regions in histological images to MR images by a registration procedure. The objective of this paper is to develop a registration technique for aligning histological and MR images of the same prostate. Material and Methods Five pairs of histological and T2-weighted MR images of radical prostatectomy specimens are collected. For each pair, registration is guided by two sets of correspondences that can be reliably established on prostate boundaries and internal salient blob-like structures of histological and MR images. Results Our developed registration method can accurately register histological and MR images. It yields results comparable to manual registration, in terms of landmark distance and volume overlap. It also outperforms both affine registration and boundary-guided registration methods. Conclusions We have developed a novel method for deformable registration of histological and MR images of the same prostate. Besides the collection of ground-truth cancer information in MR images, the method has other potential applications. An automatic, accurate registration of histological and MR images actually builds a bridge between in vivo anatomical information and ex vivo pathological information, which is valuable for various clinical studies.Publication Non-Rigid Registration between Histological and MR Images of the Prostate: A Joint Segmentation and Registration Framework(2009-06-01) Ou, Yangming; Shen, Dinggang; Feldman, Michael; Tomaszewski, John; Davatzikos, ChristosThis paper presents a 3D non-rigid registration algorithm between histological and MR images of the prostate with cancer. To compensate for the loss of 3D integrity in the histology sectioning process, series of 2D histological slices are first reconstructed into a 3D histological volume. After that, the 3D histology-MRI registration is obtained by maximizing a) landmark similarity and b) cancer region overlap between the two images. The former aims to capture distortions at prostate boundary and internal bloblike structures; and the latter aims to capture distortions specifically at cancer regions. In particular, landmark similarities, the former, is maximized by an annealing process, where correspondences between the automatically-detected boundary and internal landmarks are iteratively established in a fuzzy-to-deterministic fashion. Cancer region overlap, the latter, is maximized in a joint cancer segmentation and registration framework, where the two interleaved problems – segmentation and registration – inform each other in an iterative fashion. Registration accuracy is established by comparing against human-rater-defined landmarks and by comparing with other methods. The ultimate goal of this registration is to warp the histologically-defined cancer ground truth into MRI, for more thoroughly understanding MRI signal characteristics of the prostate cancerous tissue, which will promote the MRI-based prostate cancer diagnosis in the future studies.