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

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https://www.readbyqxmd.com/read/30159549/joint-sparse-and-low-rank-regularized-multitask-multi-linear-regression-for-prediction-of-infant-brain-development-with-incomplete-data
#1
Ehsan Adeli, Yu Meng, Gang Li, Weili Lin, Dinggang Shen
Studies involving dynamic infant brain development has received increasing attention in the past few years. For such studies, a complete longitudinal dataset is often required to precisely chart the early brain developmental trajectories. Whereas, in practice, we often face missing data at different time point(s) for different subjects. In this paper, we propose a new method for prediction of infant brain development scores at future time points based on longitudinal imaging measures at early time points with possible missing data...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/30079406/accurate-correspondence-of-cone-photoreceptor-neurons-in-the-human-eye-using-graph-matching-applied-to-longitudinal-adaptive-optics-images
#2
Jianfei Liu, HaeWon Jung, Johnny Tam
Loss of cone photoreceptor neurons is a leading cause of many blinding retinal diseases. Direct visualization of these cells in the living human eye is now feasible using adaptive optics scanning light ophthalmoscopy (AOSLO). However, it remains challenging to monitor the state of specific cells across multiple visits, due to inherent eye-motion-based distortions that arise during data acquisition, artifacts when overlapping images are montaged, as well as substantial variability in the data itself. This paper presents an accurate graph matching framework that integrates (1) robust local intensity order patterns (LIOP) to describe neuron regions with illumination variation from different visits; (2) a sparse-coding based voting process to measure visual similarities of neuron pairs using LIOP descriptors; and (3) a graph matching model that combines both visual similarity and geometrical cone packing information to determine the correspondence of repeated imaging of cone photoreceptor neurons across longitudinal AOSLO datasets...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/30035275/does-manual-delineation-only-provide-the-side-information-in-ct-prostate-segmentation
#3
Yinghuan Shi, Wanqi Yang, Yang Gao, Dinggang Shen
Prostate segmentation, for accurate prostate localization in CT images, is regarded as a crucial yet challenging task. Nevertheless, due to the inevitable factors ( e.g. , low contrast, large appearance and shape changes), the most important problem is how to learn the informative feature representation to distinguish the prostate from non-prostate regions. We address this challenging feature learning by leveraging the manual delineation as guidance: the manual delineation does not only indicate the category of patches, but also helps enhance the appearance of prostate...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/30009283/medical-image-synthesis-with-context-aware-generative-adversarial-networks
#4
Dong Nie, Roger Trullo, Jun Lian, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen
Computed tomography (CT) is critical for various clinical applications, e.g., radiation treatment planning and also PET attenuation correction in MRI/PET scanner. However, CT exposes radiation during acquisition, which may cause side effects to patients. Compared to CT, magnetic resonance imaging (MRI) is much safer and does not involve radiations. Therefore, recently researchers are greatly motivated to estimate CT image from its corresponding MR image of the same subject for the case of radiation planning...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/30009282/personalized-diagnosis-for-alzheimer-s-disease
#5
Yingying Zhu, Minjeong Kim, Xiaofeng Zhu, Jin Yan, Daniel Kaufer, Guorong Wu
Current learning-based methods for the diagnosis of Alzheimer's Disease (AD) rely on training a general classifier aiming to recognize abnormal structural alternations from homogenously distributed dataset deriving from a large population. However, due to diverse disease pathology, the real imaging data in routine clinic practices is highly complex and heterogeneous. Hence, prototype methods commonly performing well in the laboratory cannot achieve expected outcome when applied under the real clinic setting...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/30009281/joint-reconstruction-and-segmentation-of-7t-like-mr-images-from-3t-mri-based-on-cascaded-convolutional-neural-networks
#6
Khosro Bahrami, Islem Rekik, Feng Shi, Dinggang Shen
7T MRI scanner provides MR images with higher resolution and better contrast than 3T MR scanners. This helps many medical analysis tasks, including tissue segmentation. However, currently there is a very limited number of 7T MRI scanners worldwide. This motivates us to propose a novel image post-processing framework that can jointly generate high-resolution 7T-like images and their corresponding high-quality 7T-like tissue segmentation maps, solely from the routine 3T MR images. Our proposed framework comprises two parallel components, namely (1) reconstruction and (2) segmentation...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29900427/liver-tissue-classification-in-patients-with-hepatocellular-carcinoma-by-fusing-structured-and-rotationally-invariant-context-representation
#7
John Treilhard, Susanne Smolka, Lawrence Staib, Julius Chapiro, MingDe Lin, Georgy Shakirin, James Duncan
This work addresses multi-class liver tissue classification from multi-parameter MRI in patients with hepatocellular carcinoma (HCC), and is among the first to do so. We propose a structured prediction framework to simultaneously classify parenchyma, blood vessels, viable tumor tissue, and necrosis, which overcomes limitations related to classifying these tissue classes individually and consecutively. A novel classification framework is introduced, based on the integration of multi-scale shape and appearance features to initiate the classification, which is iteratively refined by augmenting the feature space with both structured and rotationally invariant label context features...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29881827/classification-of-pancreatic-cysts-in-computed-tomography-images-using-a-random-forest-and-convolutional-neural-network-ensemble
#8
Konstantin Dmitriev, Arie E Kaufman, Ammar A Javed, Ralph H Hruban, Elliot K Fishman, Anne Marie Lennon, Joel H Saltz
There are many different types of pancreatic cysts. These range from completely benign to malignant, and identifying the exact cyst type can be challenging in clinical practice. This work describes an automatic classification algorithm that classifies the four most common types of pancreatic cysts using computed tomography images. The proposed approach utilizes the general demographic information about a patient as well as the imaging appearance of the cyst. It is based on a Bayesian combination of the random forest classifier, which learns subclass-specific demographic, intensity, and shape features, and a new convolutional neural network that relies on the fine texture information...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29770368/multi-label-inductive-matrix-completion-for-joint-mgmt-and-idh1-status-prediction-for-glioma-patients
#9
Lei Chen, Han Zhang, Kim-Han Thung, Luyan Liu, Junfeng Lu, Jinsong Wu, Qian Wang, Dinggang Shen
MGMT promoter methylation and IDH1 mutation in high-grade gliomas (HGG) have proven to be the two important molecular indicators associated with better prognosis. Traditionally, the statuses of MGMT and IDH1 are obtained via surgical biopsy, which is laborious, invasive and time-consuming. Accurate presurgical prediction of their statuses based on preoperative imaging data is of great clinical value towards better treatment plan. In this paper, we propose a novel Multi-label Inductive Matrix Completion (MIMC) model, highlighted by the online inductive learning strategy, to jointly predict both MGMT and IDH1 statuses...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29756129/deep-multi-task-multi-channel-learning-for-joint-classification-and-regression-of-brain-status
#10
Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen
Jointly identifying brain diseases and predicting clinical scores have attracted increasing attention in the domain of computer-aided diagnosis using magnetic resonance imaging (MRI) data, since these two tasks are highly correlated. Although several joint learning models have been developed, most existing methods focus on using human-engineered features extracted from MRI data. Due to the possible heterogeneous property between human-engineered features and subsequent classification/regression models, those methods may lead to sub-optimal learning performance...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29577115/neighborhood-matching-for-curved-domains-with-application-to-denoising-in-diffusion-mri
#11
Geng Chen, Bin Dong, Yong Zhang, Dinggang Shen, Pew-Thian Yap
In this paper, we introduce a strategy for performing neighborhood matching on general non-Euclidean and non-flat domains. Essentially, this involves representing the domain as a graph and then extending the concept of convolution from regular grids to graphs. Acknowledging the fact that convolutions are features of local neighborhoods, neighborhood matching is carried out using the outcome of multiple convolutions at multiple scales. All these concepts are encapsulated in a sound mathematical framework, called graph framelet transforms (GFTs), which allows signals residing on non-flat domains to be decomposed according to multiple frequency subbands for rich characterization of signal patterns...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29568826/online-statistical-inference-for-large-scale-binary-images
#12
Moo K Chung, Ying Ji Chuang, Houri K Vorperian
We present a unified online statistical framework for quantifying a collection of binary images. Since medical image segmentation is often done semi-automatically, the resulting binary images may be available in a sequential manner. Further, modern medical imaging datasets are too large to fit into a computer's memory. Thus, there is a need to develop an iterative analysis framework where the final statistical maps are updated sequentially each time a new image is added to the analysis. We propose a new algorithm for online statistical inference and apply to characterize mandible growth during the first two decades of life...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29568825/-q-space-upsampling-using-x-q-space-regularization
#13
Geng Chen, Bin Dong, Yong Zhang, Dinggang Shen, Pew-Thian Yap
Acquisition time in diffusion MRI increases with the number of diffusion-weighted images that need to be acquired. Particularly in clinical settings, scan time is limited and only a sparse coverage of the vast q -space is possible. In this paper, we show how non-local self-similar information in the x - q space of diffusion MRI data can be harnessed for q -space upsampling. More specifically, we establish the relationships between signal measurements in x - q space using a patch matching mechanism that caters to unstructured data...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29568824/multimodal-hyper-connectivity-networks-for-mci-classification
#14
Yang Li, Xinqiang Gao, Biao Jie, Pew-Thian Yap, Min-Jeong Kim, Chong-Yaw Wee, Dinggang Shen
Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29568823/graph-constrained-sparse-construction-of-longitudinal-diffusion-weighted-infant-atlases
#15
Jaeil Kim, Geng Chen, Weili Lin, Pew-Thian Yap, Dinggang Shen
Constructing longitudinal diffusion-weighted atlases of infant brains poses additional challenges due to the small brain size and the dynamic changes in the early developing brains. In this paper, we introduce a novel framework for constructing longitudinally-consistent diffusion-weighted infant atlases with improved preservation of structural details and diffusion characteristics. In particular, instead of smoothing diffusion signals by simple averaging, our approach fuses the diffusion-weighted images in a patch-wise manner using sparse representation with a graph constraint that encourages spatiotemporal consistency...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29527598/locally-affine-diffeomorphic-surface-registration-for-planning-of-metopic-craniosynostosis-surgery
#16
Antonio R Porras, Beatriz Paniagua, Andinet Enquobahrie, Scott Ensel, Hina Shah, Robert Keating, Gary F Rogers, Marius George Linguraru
The outcome of cranial vault reconstruction for the surgical treatment of craniosynostosis heavily depends on the surgeon's expertise because of the lack of an objective target shape. We introduce a surface-based diffeomorphic registration framework to create the optimal post-surgical cranial shape during craniosynostosis treatment. Our framework estimates and labels where each bone piece needs to be cut using a reference template. Then, it calculates how much each bone piece needs to be translated and in which direction, using the closest normal shape from a multi-atlas as a reference...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29392246/maximum-mean-discrepancy-based-multiple-kernel-learning-for-incomplete-multimodality-neuroimaging-data
#17
Xiaofeng Zhu, Kim-Han Thung, Ehsan Adeli, Yu Zhang, Dinggang Shen
It is challenging to use incomplete multimodality data for Alzheimer's Disease (AD) diagnosis. The current methods to address this challenge, such as low-rank matrix completion ( i.e ., imputing the missing values and unknown labels simultaneously) and multi-task learning ( i.e ., defining one regression task for each combination of modalities and then learning them jointly), are unable to model the complex data-to-label relationship in AD diagnosis and also ignore the heterogeneity among the modalities. In light of this, we propose a new Maximum Mean Discrepancy (MMD) based Multiple Kernel Learning (MKL) method for AD diagnosis using incomplete multimodality data...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29379899/fast-geodesic-regression-for-population-based-image-analysis
#18
Yi Hong, Polina Golland, Miaomiao Zhang
Geodesic regression on images enables studies of brain development and degeneration, disease progression, and tumor growth. The high-dimensional nature of image data presents significant computational challenges for the current regression approaches and prohibits large scale studies. In this paper, we present a fast geodesic regression method that dramatically decreases the computational cost of the inference procedure while maintaining prediction accuracy. We employ an efficient low dimensional representation of diffeomorphic transformations derived from the image data and characterize the regressed trajectory in the space of diffeomorphisms by its initial conditions, i...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29376150/joint-craniomaxillofacial-bone-segmentation-and-landmark-digitization-by-context-guided-fully-convolutional-networks
#19
MULTICENTER STUDY
Jun Zhang, Mingxia Liu, Li Wang, Si Chen, Peng Yuan, Jianfu Li, Steve Guo-Fang Shen, Zhen Tang, Ken-Chung Chen, James J Xia, Dinggang Shen
Generating accurate 3D models from cone-beam computed tomography (CBCT) images is an important step in developing treatment plans for patients with craniomaxillofacial (CMF) deformities. This process often involves bone segmentation and landmark digitization. Since anatomical landmarks generally lie on the boundaries of segmented bone regions, the tasks of bone segmentation and landmark digitization could be highly correlated. However, most existing methods simply treat them as two standalone tasks, without considering their inherent association...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29354811/unsupervised-discovery-of-spatially-informed-lung-texture-patterns-for-pulmonary-emphysema-the-mesa-copd-study
#20
Jie Yang, Elsa D Angelini, Pallavi P Balte, Eric A Hoffman, John H M Austin, Benjamin M Smith, Jingkuan Song, R Graham Barr, Andrew F Laine
Unsupervised discovery of pulmonary emphysema subtypes offers the potential for new definitions of emphysema on lung computed tomography (CT) that go beyond the standard subtypes identified on autopsy. Emphysema subtypes can be defined on CT as a variety of textures with certain spatial prevalence. However, most existing approaches for learning emphysema subtypes on CT are limited to texture features, which are sub-optimal due to the lack of spatial information. In this work, we exploit a standardized spatial mapping of the lung and propose a novel framework for combining spatial and texture information to discover spatially-informed lung texture patterns (sLTPs)...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
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