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

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https://read.qxmd.com/read/30734031/using-the-anisotropic-laplace-equation-to-compute-cortical-thickness
#1
Anand A Joshi, Chitresh Bhushan, Ronald Salloum, Jessica Wisnowski, David W Shattuck, Richard M Leahy
Automatic computation of cortical thickness is a critical step when investigating neuroanatomical population differences and changes associated with normal development and aging, as well as in neurodegenerative diseases including Alzheimer's and Parkinson's. Limited spatial resolution and partial volume effects, in which more than one tissue type is represented in each voxel, have a significant impact on the accuracy of thickness estimates, particularly if a hard intensity threshold is used to delineate cortical boundaries...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30714047/rfdemons-resting-fmri-based-cortical-surface-registration-using-the-brainsync-transform
#2
Anand A Joshi, Jian Li, Minqi Chong, Haleh Akrami, Richard M Leahy
Cross subject functional studies of cerebral cortex require cortical registration that aligns functional brain regions. While cortical folding patterns are approximate indicators of the underlying cytoarchitecture, coregistration based on these features alone does not accurately align functional regions in cerebral cortex. This paper presents a method for cortical surface registration (rfDemons) based on resting fMRI (rfMRI) data that uses curvature-based anatomical registration as an initialization. In contrast to existing techniques that use connectivity-based features derived from rfMRI, the proposed method uses 'synchronized' resting rfMRI time series directly...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30693351/conditional-generative-adversarial-networks-for-metal-artifact-reduction-in-ct-images-of-the-ear
#3
Jianing Wang, Yiyuan Zhao, Jack H Noble, Benoit M Dawant
We propose an approach based on a conditional generative adversarial network (cGAN) for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients. Our training set contains paired pre-implantation and post-implantation CTs of 90 ears. At the training phase, the cGAN learns a mapping from the artifact-affected CTs to the artifact-free CTs. At the inference phase, given new metal-artifact-affected CTs, the cGAN produces CTs in which the artifacts are removed...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30627709/adversarial-similarity-network-for-evaluating-image-alignment-in-deep-learning-based-registration
#4
Jingfan Fan, Xiaohuan Cao, Zhong Xue, Pew-Thian Yap, Dinggang Shen
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration frameworks, our approach does not require ground-truth deformations and specific similarity metrics. We connect a registration network and a discrimination network with a deformable transformation layer. The registration network is trained with feedback from the discrimination network, which is designed to judge whether a pair of registered images are sufficiently similar. Using adversarial training, the registration network is trained to predict deformations that are accurate enough to fool the discrimination network...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30569040/efficient-groupwise-registration-of-mr-brain-images-via-hierarchical-graph-set-shrinkage
#5
Pei Dong, Xiaohuan Cao, Pew-Thian Yap, Dinggang Shen
Accurate and efficient groupwise registration is important for population analysis. Current groupwise registration methods suffer from high computational cost, which hinders their application to large image datasets. To alleviate the computational burden while delivering accurate groupwise registration result, we propose to use a hierarchical graph set to model the complex image distribution with possibly large anatomical variations, and then turn the groupwise registration problem as a series of simple-to-solve graph shrinkage problems...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30465047/adversarial-domain-adaptation-for-classification-of-prostate-histopathology-whole-slide-images
#6
Jian Ren, Ilker Hacihaliloglu, Eric A Singer, David J Foran, Xin Qi
Automatic and accurate Gleason grading of histopathology tissue slides is crucial for prostate cancer diagnosis, treatment, and prognosis. Usually, histopathology tissue slides from different institutions show heterogeneous appearances because of different tissue preparation and staining procedures, thus the predictable model learned from one domain may not be applicable to a new domain directly. Here we propose to adopt unsupervised domain adaptation to transfer the discriminative knowledge obtained from the source domain to the target domain with-out requiring labeling of images at the target domain...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30450495/craniomaxillofacial-bony-structures-segmentation-from-mri-with-deep-supervision-adversarial-learning
#7
Miaoyun Zhao, Li Wang, Jiawei Chen, Dong Nie, Yulai Cong, Sahar Ahmad, Angela Ho, Peng Yuan, Steve H Fung, Hannah H Deng, James Xia, Dinggang Shen
Automatic segmentation of medical images finds abundant applications in clinical studies. Computed Tomography (CT) imaging plays a critical role in diagnostic and surgical planning of craniomaxillofacial (CMF) surgeries as it shows clear bony structures. However, CT imaging poses radiation risks for the subjects being scanned. Alternatively, Magnetic Resonance Imaging (MRI) is considered to be safe and provides good visualization of the soft tissues, but the bony structures appear invisible from MRI. Therefore, the segmentation of bony structures from MRI is quite challenging...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30430147/volume-based-analysis-of-6-month-old-infant-brain-mri-for-autism-biomarker-identification-and-early-diagnosis
#8
Li Wang, Gang Li, Feng Shi, Xiaohuan Cao, Chunfeng Lian, Dong Nie, Mingxia Liu, Han Zhang, Guannan Li, Zhengwang Wu, Weili Lin, Dinggang Shen
Autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Due to the absence of early biomarkers to detect infants either with or at-risk of ASD during the first postnatal year of life, diagnosis must rely on behavioral observations long after birth. As a result, the window of opportunity for effective intervention may have passed when the disorder is detected. Therefore, it is clinically urgent to identify imaging-based biomarkers for early diagnosis and intervention...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30345432/identification-of-multi-scale-hierarchical-brain-functional-networks-using-deep-matrix-factorization
#9
Hongming Li, Xiaofeng Zhu, Yong Fan
We present a deep semi-nonnegative matrix factorization method for identifying subject-specific functional networks (FNs) at multiple spatial scales with a hierarchical organization from resting state fMRI data. Our method is built upon a deep semi-nonnegative matrix factorization framework to jointly detect the FNs at multiple scales with a hierarchical organization, enhanced by group sparsity regularization that helps identify subject-specific FNs without loss of inter-subject comparability. The proposed method has been validated for predicting subject-specific functional activations based on functional connectivity measures of the hierarchical multi-scale FNs of the same subjects...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30338317/a-tetrahedron-based-heat-flux-signature-for-cortical-thickness-morphometry-analysis
#10
Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
Cortical thickness analysis of brain magnetic resonance images is an important technique in neuroimaging research. There are two main computational paradigms, namely voxel-based and surface-based methods. Recently, a tetrahedron-based volumetric morphometry (TBVM) approach involving proper discretization methods was proposed. The multi-scale and physics-based geometric features generated through such methods may yield stronger statistical power. However, several challenges, such as the lack of well-defined thickness statistics and the difficulty in filling tetrahedrons into the thin and curvy cortex structure, impede the broad application of TBVM...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30320311/brain-decoding-from-functional-mri-using-long-short-term-memory-recurrent-neural-networks
#11
Hongming Li, Yong Fan
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. Promising performance has been achieved using brain functional connectivity or brain activation signatures for a variety of brain decoding tasks. However, most of existing studies have built decoding models upon features extracted from imaging data at individual time points or temporal windows with a fixed interval, which might not be optimal across different cognitive processes due to varying temporal durations and dependency of different cognitive processes...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30320310/identification-of-temporal-transition-of-functional-states-using-recurrent-neural-networks-from-functional-mri
#12
Hongming Li, Yong Fan
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes. Besides sliding window based approaches, a variety of methods have been developed to automatically split the entire functional MRI scan into segments by detecting change points of functional signals to facilitate better characterization of temporally dynamic functional connectivity patterns. However, these methods are based on certain assumptions for the functional signals, such as Gaussian distribution, which are not necessarily suitable for the fMRI data...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30294726/tumor-aware-adversarial-domain-adaptation-from-ct-to-mri-for-lung-cancer-segmentation
#13
Jue Jiang, Yu-Chi Hu, Neelam Tyagi, Pengpeng Zhang, Andreas Rimner, Gig S Mageras, Joseph O Deasy, Harini Veeraraghavan
We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30272058/multimodal-fusion-of-brain-networks-with-longitudinal-couplings
#14
Wen Zhang, Kai Shu, Suhang Wang, Huan Liu, Yalin Wang
In recent years, brain network analysis has attracted considerable interests in the field of neuroimaging analysis. It plays a vital role in understanding biologically fundamental mechanisms of human brains. As the upward trend of multi-source in neuroimaging data collection, effective learning from the different types of data sources, e.g. multimodal and longitudinal data, is much in demand. In this paper, we propose a general coupling framework, the multimodal neuroimaging network fusion with longitudinal couplings ( MMLC ), to learn the latent representations of brain networks...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://read.qxmd.com/read/30159549/joint-sparse-and-low-rank-regularized-multitask-multi-linear-regression-for-prediction-of-infant-brain-development-with-incomplete-data
#15
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://read.qxmd.com/read/30079406/accurate-correspondence-of-cone-photoreceptor-neurons-in-the-human-eye-using-graph-matching-applied-to-longitudinal-adaptive-optics-images
#16
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://read.qxmd.com/read/30035275/does-manual-delineation-only-provide-the-side-information-in-ct-prostate-segmentation
#17
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://read.qxmd.com/read/30009283/medical-image-synthesis-with-context-aware-generative-adversarial-networks
#18
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://read.qxmd.com/read/30009282/personalized-diagnosis-for-alzheimer-s-disease
#19
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://read.qxmd.com/read/30009281/joint-reconstruction-and-segmentation-of-7t-like-mr-images-from-3t-mri-based-on-cascaded-convolutional-neural-networks
#20
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 ..
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