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Information Processing in Medical Imaging: Proceedings of the ... Conference

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https://www.readbyqxmd.com/read/26257500/sampling-from-determinantal-point-processes-for-scalable-manifold-learning
#1
Christian Wachinger, Polina Golland
High computational costs of manifold learning prohibit its application for large datasets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the Nyström method. The two main challenges that arise are: (i) the landmarks selected in non-Euclidean geometries must result in a low reconstruction error, (ii) the graph constructed from sparsely sampled landmarks must approximate the manifold well. We propose to sample the landmarks from determinantal distributions on non-Euclidean spaces...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26257499/keypoint-transfer-segmentation
#2
C Wachinger, M Toews, G Langs, W Wells, P Golland
We present an image segmentation method that transfers label maps of entire organs from the training images to the novel image to be segmented. The transfer is based on sparse correspondences between keypoints that represent automatically identified distinctive image locations. Our segmentation algorithm consists of three steps: (i) keypoint matching, (ii) voting-based keypoint labeling, and (iii) keypoint-based probabilistic transfer of organ label maps. We introduce generative models for the inference of keypoint labels and for image segmentation, where keypoint matches are treated as a latent random variable and are marginalized out as part of the algorithm...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26257498/generative-method-to-discover-genetically-driven-image-biomarkers
#3
Nematollah K Batmanghelich, Ardavan Saeedi, Michael Cho, Raul San Jose Estepar, Polina Golland
We present a generative probabilistic approach to discovery of disease subtypes determined by the genetic variants. In many diseases, multiple types of pathology may present simultaneously in a patient, making quantification of the disease challenging. Our method seeks common co-occurring image and genetic patterns in a population as a way to model these two different data types jointly. We assume that each patient is a mixture of multiple disease subtypes and use the joint generative model of image and genetic markers to identify disease subtypes guided by known genetic influences...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26236122/a-riemannian-framework-for-intrinsic-comparison-of-closed-genus-zero-shapes
#4
Boris Gutman, Thomas Fletcher, M Jorge Cardoso, Greg Fleishman, Marco Lorenzi, Paul Thompson, Sebastien Ourselin
We present a framework for intrinsic comparison of surface metric structures and curvatures. This work parallels the work of Kurtek et al. on parameterization-invariant comparison of genus zero shapes. Here, instead of comparing the embedding of spherically parameterized surfaces in space, we focus on the first fundamental form. To ensure that the distance on spherical metric tensor fields is invariant to parameterization, we apply the conjugation-invariant metric arising from the L(2) norm on symmetric positive definite matrices...
July 2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26419020/proceedings-of-the-24th-international-information-processing-in-medical-imaging-conference-ipmi-2015-june-28-july-3-2015-isle-of-skye-united-kingdom
#5
(no author information available yet)
No abstract text is available yet for this article.
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26223048/multiple-orderings-of-events-in-disease-progression
#6
Alexandra L Young, Neil P Oxtoby, Jonathan Huang, Razvan V Marinescu, Pankaj Daga, David M Cash, Nick C Fox, Sebastien Ourselin, Jonathan M Schott, Daniel C Alexander
The event-based model constructs a discrete picture of disease progression from cross-sectional data sets, with each event corresponding to a new biomarker becoming abnormal. However, it relies on the assumption that all subjects follow a single event sequence. This is a major simplification for sporadic disease data sets, which are highly heterogeneous, include distinct subgroups, and contain significant proportions of outliers. In this work we relax this assumption by considering two extensions to the event-based model: a generalised Mallows model, which allows subjects to deviate from the main event sequence, and a Dirichlet process mixture of generalised Mallows models, which models clusters of subjects that follow different event sequences, each of which has a corresponding variance...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26223047/ground-truth-for-diffusion-mri-in-cancer-a-model-based-investigation-of-a-novel-tissue-mimetic-material
#7
Damien J McHugh, Fenglei Zhou, Penny L Hubbard Cristinacce, Josephine H Naish, Geoffrey J M Parker
This work presents preliminary results on the development, characterisation, and use of a novel physical phantom designed as a simple mimic of tumour cellular structure, for diffusion-weighted magnetic resonance imaging (DW-MRI) applications. The phantom consists of a collection of roughly spherical, micron-sized core-shell polymer 'cells', providing a system whose ground truth microstructural properties can be determined and compared with those obtained from modelling the DW-MRI signal. A two-compartment analytic model combining restricted diffusion inside a sphere with hindered extracellular diffusion was initially investigated through Monte Carlo diffusion simulations, allowing a comparison between analytic and simulated signals...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221718/joint-6d-k-q-space-compressed-sensing-for-accelerated-high-angular-resolution-diffusion-mri
#8
Jian Cheng, Dinggang Shen, Peter J Basser, Pew-Thian Yap
High Angular Resolution Diffusion Imaging (HARDI) avoids the Gaussian. diffusion assumption that is inherent in Diffusion Tensor Imaging (DTI), and is capable of characterizing complex white matter micro-structure with greater precision. However, HARDI methods such as Diffusion Spectrum Imaging (DSI) typically require significantly more signal measurements than DTI, resulting in prohibitively long scanning times. One of the goals in HARDI research is therefore to improve estimation of quantities such as the Ensemble Average Propagator (EAP) and the Orientation Distribution Function (ODF) with a limited number of diffusion-weighted measurements...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221717/coupled-stable-overlapping-replicator-dynamics-for-multimodal-brain-subnetwork-identification
#9
Burak Yoldemir, Bernard Ng, Rafeef Abugharbieh
Combining imaging modalities to synthesize their inherent strengths provides a promising means for improving brain subnetwork identification. We propose a multimodal integration technique based on a sex-differentiated formulation of replicator dynamics for identifying subnetworks of brain regions that exhibit high inter-connectivity both functionally and structurally. Our method has a number of desired properties, namely, it can operate on weighted graphs derived from functional magnetic resonance imaging (tMRI) and diffusion MRI (dMRI) data, allows for subnetwork overlaps, has an intrinsic criterion for setting the number of subnetworks, and provides statistical control on false node inclusion in the identified subnetworks via the incorporation of stability selection...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221716/tree-encoded-conditional-random-fields-for-image-synthesis
#10
Amod Jog, Aaron Carass, Dzung L Pham, Jerry L Prince
Magnetic resonance imaging (MRI) is the dominant modality for neuroimaging in clinical and research domains. The tremendous versatility of MRI as a modality can lead to large variability in terms of image contrast, resolution, noise, and artifacts. Variability can also manifest itself as missing or corrupt imaging data. Image synthesis has been recently proposed to homogenize and/or enhance the quality of existing imaging data in order to make them more suitable as consistent inputs for processing. We frame the image synthesis problem as an inference problem on a 3-D continuous-valued conditional random field (CRF)...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221715/construction-of-an-unbiased-spatio-temporal-atlas-of-the-tongue-during-speech
#11
Jonghye Woo, Fangxu Xing, Junghoon Lee, Maureen Stone, Jerry L Prince
Quantitative characterization and comparison of tongue motion during speech and swallowing present fundamental challenges because of striking variations in tongue structure and motion across subjects. A reliable and objective description of the dynamics tongue motion requires the consistent integration of inter-subject variability to detect the subtle changes in populations. To this end, in this work, we present an approach to constructing an unbiased spatio-temporal atlas of the tongue during speech for the first time, based on cine-MRI from twenty two normal subjects...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221714/model-based-estimation-of-microscopic-anisotropy-in-macroscopically-isotropic-substrates-using-diffusion-mri
#12
Andrada Ianuş, Ivana Drobnjak, Daniel C Alexander
Non-invasive estimation of cell size and shape is a key challenge in diffusion MRI. Changes in cell size and shape discriminate functional areas in the brain and can highlight different degrees of malignancy in cancer tumours. Consequently various methods have emerged recently that aim to measure the microscopic anisotropy of porous media such as biological tissue and aim to reflect pore eccentricity, the simplest shape feature. However, current methods assume a substrate of identical pores, and are strongly influenced by non-trivial size distribution...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221713/sampling-from-determinantal-point-processes-for-scalable-manifold-learning
#13
Christian Wachinger, Polina Golland
High computational costs of manifold learning prohibit its application for large datasets. A common strategy to overcome this problem is to perform dimensionality reduction on selected landmarks and to successively embed the entire dataset with the Nyström method. The two main challenges that arise are: (i) the landmarks selected in non-Euclidean geometries must result in a low reconstruction error, (ii) the graph constructed from sparsely sampled landmarks must approximate the manifold well. We propose to sample the landmarks from determinantal distributions on non-Euclidean spaces...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221712/axtract-microstructure-driven-tractography-based-on-the-ensemble-average-propagator
#14
Gabriel Girard, Rutger Fick, Maxime Descoteaux, Rachid Deriche, Demian Wassermann
We propose a novel method to simultaneously trace brain white matter (WM) fascicles and estimate WM microstructure characteristics. Recent advancements in diffusion-weighted imaging (DWI) allow multi-shell acquisitions with b-values of up to 10,000 s/mm2 in human subjects, enabling the measurement of the ensemble average propagator (EAP) at distances as short as 10 μm. Coupled with continuous models of the full 3D DWI signal and the EAP such as Mean Apparent Propagator (MAP) MRI, these acquisition schemes provide unparalleled means to probe the WM tissue in vivo...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221711/segmenting-the-brain-surface-from-ct-images-with-artifacts-using-dictionary-learning-for-non-rigid-mr-ct-registration
#15
John A Onofrey, Lawrence H Staib, Xenophon Papademetris
This paper presents a dictionary learning-based method to segment the brain surface in post-surgical CT images of epilepsy patients following surgical implantation of electrodes. Using the electrodes identified in the post-implantation CT, surgeons require accurate registration with pre-implantation functional and structural MR imaging to guide surgical resection of epileptic tissue. In this work, we use a surface-based registration method to align the MR and CT brain surfaces. The key challenge here is not the registration, but rather the extraction of the cortical surface from the CT image, which includes missing parts of the skull and artifacts introduced by the electrodes...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221710/towards-a-quantified-network-portrait-of-a-population
#16
Birkan Tunç, Varsha Shankar, Drew Parker, Robert T Schultz, Ragini Verma
Computational network analysis has enabled researchers to investigate patterns of interactions between anatomical regions of the brain. Identification of subnetworks of the human connectome can reveal how the network manages an interplay of the seemingly competing principles of functional segregation and integration. Despite the study of subnetworks of the human structural connectome by various groups, the level of expression of these subnetworks in each subject remains for the most part largely unexplored...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221709/a-simulation-framework-for-quantitative-validation-of-artefact-correction-in-diffusion-mri
#17
Mark S Graham, Ivana Drobnjak, Hui Zhang
In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221708/efficient-gaussian-process-based-modelling-and-prediction-of-image-time-series
#18
Marco Lorenzi, Gabriel Ziegler, Daniel C Alexander, Sebastien Ourselin
In this work we propose a novel Gaussian process-based spatio-temporal model of time series of images. By assuming separability of spatial and temporal processes we provide a very efficient and robust formulation for the marginal likelihood computation and the posterior prediction. The model adaptively accounts for local spatial correlations of the data, and the covariance structure is effectively parameterised by the Kronecker product of covariance matrices of very small size, each encoding only a single direction in space...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221707/illumination-compensation-and-normalization-using-low-rank-decomposition-of-multispectral-images-in-dermatology
#19
Alexandru Duliu, Richard Brosig, Saahil Ognawala, Tobias Lasser, Mahzad Ziai, Nassir Navab
When attempting to recover the surface color from an image, modelling the illumination contribution per-pixel is essential. In this work we present a novel approach for illumination compensation using multispectral image data. This is done by means of a low-rank decomposition of representative spectral bands with prior knowledge of the reflectance spectra of the imaged surface. Experimental results on synthetic data, as well as on images of real lesions acquired at the university clinic, show that the proposed method significantly improves the contrast between the lesion and the background...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
https://www.readbyqxmd.com/read/26221706/tractography-driven-groupwise-multi-scale-parcellation-of-the-cortex
#20
Sarah Parisot, Salim Arslan, Jonathan Passerat-Palmbach, William M Wells, Daniel Rueckert
The analysis of the connectome of the human brain provides key insight into the brain's organisation and function, and its evolution in disease or ageing. Parcellation of the cortical surface into distinct regions in terms of structural connectivity is an essential step that can enable such analysis. The estimation of a stable connectome across a population of healthy subjects requires the estimation of a groupwise parcellation that can capture the variability of the connectome across the population. This problem has solely been addressed in the literature via averaging of connectivity profiles or finding correspondences between individual parcellations a posteriori...
2015: Information Processing in Medical Imaging: Proceedings of the ... Conference
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