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Medical Image Computing and Computer-assisted Intervention: MICCAI ...

Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang, Lin Yang
Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Hengameh Mirzaalian, Amicie de Pierrefeu, Peter Savadjiev, Ofer Pasternak, Sylvain Bouix, Marek Kubicki, Carl-Fredrik Westin, Martha E Shenton, Yogesh Rathi
Harmonizing diffusion MRI (dMRI) images across multiple sites is imperative for joint analysis of the data to significantly increase the sample size and statistical power of neuroimaging studies. In this work, we develop a method to harmonize diffusion MRI data across multiple sites and scanners that incorporates two main novelties: i) we take into account the spatial variability of the signal (for different sites) in different parts of the brain as opposed to existing methods, which consider one linear statistical covariate for the entire brain; ii) our method is model-free, in that no a-priori model of diffusion (e...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Fitsum Mesadi, Mujdat Cetin, Tolga Tasdizen
The use of appearance and shape priors in image segmentation is known to improve accuracy; however, existing techniques have several drawbacks. Active shape and appearance models require landmark points and assume unimodal shape and appearance distributions. Level set based shape priors are limited to global shape similarity. In this paper, we present a novel shape and appearance priors for image segmentation based on an implicit parametric shape representation called disjunctive normal shape model (DNSM). DNSM is formed by disjunction of conjunctions of half-spaces defined by discriminants...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yong Zhang, Kilian M Pohl
We propose an algorithm to distinguish 3D+t images of healthy from diseased subjects by solving logistic regression based on cardinality constrained, group sparsity. This method reduces the risk of overfitting by providing an elegant solution to identifying anatomical regions most impacted by disease. It also ensures that consistent identification across the time series by grouping each image feature across time and counting the number of non-zero groupings. While popular in medical imaging, group cardinality constrained problems are generally solved by relaxing counting with summing over the groupings...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yu Meng, Gang Li, Weili Lin, John H Gilmore, Dinggang Shen
Analysis of anatomical covariance for cortex morphology in individual subjects plays an important role in the study of human brains. However, the approaches for constructing individual structural networks have not been well developed yet. Existing methods based on patch-wise image intensity similarity suffer from several major drawbacks, i.e., 1) violation of cortical topological properties, 2) sensitivity to intensity heterogeneity, and 3) influence by patch size heterogeneity. To overcome these limitations, this paper presents a novel cortical surface-based method for constructing individual structural networks...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Gang Li, Li Wang, John H Gilmore, Weili Lin, Dinggang Shen
Cortical surface atlases, equipped with anatomically and functionally defined parcellations, are of fundamental importance in neuroimaging studies. Typically, parcellations of surface atlases are derived based on the sulcal-gyral landmarks, which are extremely variable across individuals and poorly matched with microstructural and functional boundaries. Cortical developmental trajectories in infants reflect underlying changes of microstructures, which essentially determines the molecular organization and functional principles of the cortex, thus allowing better definition of developmentally, microstructurally, and functionally distinct regions, compared to conventional sulcal-gyral landmarks...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yuyao Zhang, Feng Shi, Pew-Thian Yap, Dinggang Shen
Brain atlases are an integral component of neuroimaging studies. However, most brain atlases are fuzzy and lack structural details, especially in the cortical regions. In particular, neonatal brain atlases are especially challenging to construct due to the low spatial resolution and low tissue contrast. This is mainly caused by the image averaging process involved in atlas construction, often smoothing out high-frequency contents that indicate fine anatomical details. In this paper, we propose a novel framework for detail-preserving construction of atlases...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Islem Rekik, Gang Li, Weili Lin, Dinggang Shen
Cortical surface registration or matching facilitates atlasing, cortical morphology-function comparison and statistical analysis. Methods that geodesically shoot surfaces into one another, as currents or varifolds, provide an elegant mathematical framework for generic surface matching and dynamic local features estimation, such as deformation momenta. However, conventional current and varifold matching methods only use the normals of the surface to measure its geometry and guide the warping process, which overlooks the importance of the direction in the convoluted cortical sulcal and gyral folds...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Jack H Noble, Benoit M Dawant
Cochlear Implants (CIs) restore hearing using an electrode array that is surgically implanted into the cochlea. Research has indicated there is a link between electrode location within the cochlea and hearing outcomes, however, comprehensive analysis of this phenomenon has not been possible because techniques proposed for locating electrodes only work for specific implant models or are too labor intensive to be applied on large datasets. We present a general and automatic graph-based method for localizing electrode arrays in CTs that is effective for various implant models...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Frank Preiswerk, Matthew Toews, W Scott Hoge, Jr-Yuan George Chiou, Lawrence P Panych, William M Wells, Bruno Madore
Magnetic Resonance (MR) imaging provides excellent image quality at a high cost and low frame rate. Ultrasound (US) provides poor image quality at a low cost and high frame rate. We propose an instance-based learning system to obtain the best of both worlds: high quality MR images at high frame rates from a low cost single-element US sensor. Concurrent US and MRI pairs are acquired during a relatively brief offine learning phase involving the US transducer and MR scanner. High frame rate, high quality MR imaging of respiratory organ motion is then predicted from US measurements, even after stopping MRI acquisition, using a probabilistic kernel regression framework...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Monami Banerjee, Rudrasis Chakraborty, Edward Ofori, David Vaillancourt, Baba C Vemuri
Regression in its most common form where independent and dependent variables are in ℝ (n) is a ubiquitous tool in Sciences and Engineering. Recent advances in Medical Imaging has lead to a wide spread availability of manifold-valued data leading to problems where the independent variables are manifold-valued and dependent are real-valued or vice-versa. The most common method of regression on a manifold is the geodesic regression, which is the counterpart of linear regression in Euclidean space. Often, the relation between the variables is highly complex, and existing most commonly used geodesic regression can prove to be inaccurate...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Kim-Han Thung, Pew-Thian Yap, Ehsan Adeli-M, Dinggang Shen
Identifying progressive mild cognitive impairment (pMCI) patients and predicting when they will convert to Alzheimer's disease (AD) are important for early medical intervention. Multi-modality and longitudinal data provide a great amount of information for improving diagnosis and prognosis. But these data are often incomplete and noisy. To improve the utility of these data for prediction purposes, we propose an approach to denoise the data, impute missing values, and cluster the data into low-dimensional subspaces for pMCI prediction...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yue Gao, Ehsan Adeli-M, Minjeong Kim, Panteleimon Giannakopoulos, Sven Haller, Dinggang Shen
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder that can lead to progressive memory loss and cognition impairment. Therefore, diagnosing AD during the risk stage, a.k.a. Mild Cognitive Impairment (MCI), has attracted ever increasing interest. Besides the automated diagnosis of MCI, it is important to provide physicians with related MCI cases with visually similar imaging data for case-based reasoning or evidence-based medicine in clinical practices. To this end, we propose a multi-graph learning based medical image retrieval technique for MCI diagnostic assistance...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Heung-Il Suk, Seong-Whan Lee, Dinggang Shen
In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yanrong Guo, Guorong Wu, Pew-Thian Yap, Valerie Jewells, Weili Lin, Dinggang Shen
Aberrant development of the human brain during the first year after birth is known to cause critical implications in later stages of life. In particular, neuropsychiatric disorders, such as attention deficit hyperactivity disorder (ADHD), have been linked with abnormal early development of the hippocampus. Despite its known importance, studying the hippocampus in infant subjects is very challenging due to the significantly smaller brain size, dynamically varying image contrast, and large across-subject variation...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Guangkai Ma, Yaozong Gao, Guorong Wu, Ligang Wu, Dinggang Shen
Labeling MR brain images into anatomically meaningful regions is important in many quantitative brain researches. In many existing label fusion methods, appearance information is widely used. Meanwhile, recent progress in computer vision suggests that the context feature is very useful in identifying an object from a complex scene. In light of this, we propose a novel learning-based label fusion method by using both low-level appearance features (computed from the target image) and high-level context features (computed from warped atlases or tentative labeling maps of the target image)...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Sang Hyun Park, Yaozong Gao, Dinggang Shen
We propose a novel multi-atlas based segmentation method to address the editing scenario, when given an incomplete segmentation along with a set of training label images. Unlike previous multi-atlas based methods, which depend solely on appearance features, we incorporate interaction-guided constraints to find appropriate training labels and derive their voting weights. Specifically, we divide user interactions, provided on erroneous parts, into multiple local interaction combinations, and then locally search for the training label patches well-matched with each interaction combination and also the previous segmentation...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yantao Song, Guorong Wu, Quansen Sun, Khosro Bahrami, Chunming Li, Dinggang Shen
Accurate segmentation of anatomical structures in medical images is very important in neuroscience studies. Recently, multi-atlas patch-based label fusion methods have achieved many successes, which generally represent each target patch from an atlas patch dictionary in the image domain and then predict the latent label by directly applying the estimated representation coefficients in the label domain. However, due to the large gap between these two domains, the estimated representation coefficients in the image domain may not stay optimal for the label fusion...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Yue Gao, Chong-Yaw Wee, Minjeong Kim, Panteleimon Giannakopoulos, Marie-Louise Montandon, Sven Haller, Dinggang Shen
The identification of subtle brain changes that are associated with mild cognitive impairment (MCI), the at-risk stage of Alzheimer's disease, is still a challenging task. Different from existing works, which employ multimodal data (e.g., MRI, PET or CSF) to identify MCI subjects from normal elderly controls, we use four MRI sequences, including T1-weighted MRI (T1), Diffusion Tensor Imaging (DTI), Resting-State functional MRI (RS-fMRI) and Arterial Spin Labeling (ASL) perfusion imaging. Since these MRI sequences simultaneously capture various aspects of brain structure and function during clinical routine scan, it simplifies finding the relationship between subjects by incorporating the mutual information among them...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
George H Chen, Devavrat Shah, Polina Golland
Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work. We bridge this gap by providing a theoretical performance guarantee for nearest-neighbor and weighted majority voting segmentation under a new probabilistic model for patch-based image segmentation. Our analysis relies on a new local property for how similar nearby patches are, and fuses existing lines of work on modeling natural imagery patches and theory for nonparametric classification...
October 2015: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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