Davatzikos, Christos
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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 Brain-Tumor Interaction Biophysical Models for Medical Image Registration(2008-12-01) Davatzikos, Christos; Hogea, Cosmina; Biros, GeorgeState-of-the art algorithms for deformable image registration are based on the minimization of an image similarity functional that is regularized by adding a penalty term on the deformation map. The penalty function typically represents a smoothness regularization. In this article, we use a constrained optimization formulation in which the image similarity functional is coupled to a biophysical model. This formulation is pertinent when the data have been generated by imaging tissue that undergoes deformations due to an actual biophysical phenomenon. Such is the case of coregistering tumor-bearing brain images from the same individual. We present an approximate model that couples tumor growth with the mechanical deformations of the surrounding brain tissue. We consider primary brain tumors—in particular, gliomas. Glioma growth is modeled by a reaction-advection-diffusion PDE, with a two-way coupling with the underlying tissue elastic deformation. Tumor bulk, infiltration, and subsequent mass effects are not regarded separately but are captured by the model itself in the course of its evolution. Our formulation allows for updating the tumor diffusion coefficient following structural displacements caused by tumor growth/infiltration. Our forward problem implementation builds on the PETSc library of Argonne National Laboratory. Our reformulation results in a very small parameter space, and we use the derivative-free optimization library APPSPACK of Sandia National Laboratories. We test the forward model and the optimization framework by using landmark-based similarity functions and by applying it to brain tumor data from clinical and animal studies. State-of-the-art registration algorithms fail in such problems due to excessive deformations. We compare our results with previous work in our group, and we observed up to 50% improvement in landmark deformation prediction. We present preliminary validation results in which we were able to reconstruct deformation fields using four degrees of freedom. Our study demonstrates the validity of our formulation and points to the need for richer datasets and fast optimization algorithms.Publication DRAMMS: deformable registration via attribute matching and mutual-saliency weighting(2009-07-01) Ou, Yangming; Davatzikos, ChristosA general-purpose deformable registration algorithm referred to as ”DRAMMS” is presented in this paper. DRAMMS adds to the literature of registration methods that bridge between the traditional voxel-wise methods and landmark/feature-based methods. In particular, DRAMMS extracts Gabor attributes at each voxel and selects the optimal components, so that they form a highly distinctive morphological signature reflecting the anatomical context around each voxel in a multi-scale and multi-resolution fashion. Compared with intensity or mutual-information based methods, the high-dimensional optimal Gabor attributes render different anatomical regions relatively distinctively identifiable and therefore help establish more accurate and reliable correspondence. Moreover, the optimal Gabor attribute vector is constructed in a way that generalizes well, i.e., it can be applied to different registration tasks, regardless of the image contents under registration. A second characteristic of DRAMMS is that it is based on a cost function that weights different voxel pairs according to a metric referred to as ”mutual-saliency”, which reflects the uniqueness (reliability) of anatomical correspondences implied by the tentative transformation. As a result, image voxels do not contribute equally to the optimization process, as in most voxel-wise methods, or in a binary selection fashion, as in most landmark/feature-based methods. Instead, they contribute according to a continuously-valued mutual-saliency map, which is dynamically updated during the algorithm’s evolution. The general applicability and accuracy of DRAMMS are demonstrated by experiments in simulated images, inter-subject images, single-/multi-modality images, and longitudinal images, from human and mouse brains, breast, heart, and prostate.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 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 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 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 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 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.