Cortical Surface Parcellation using Spherical Convolutional Neural Networks.
We present cortical surface parcellation using spherical deep convolutional neural networks. Traditional multi-atlas cortical surface parcellation requires inter-subject surface registration using geometric features with slow processing speed on a single subject (2-3 hours). Moreover, even optimal surface registration does not necessarily produce optimal cortical parcellation as parcel boundaries are not fully matched to the geometric features. In this context, a choice of training features is important for accurate cortical parcellation. To utilize the networks efficiently, we propose cortical parcellation-specific input ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 7, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Hierarchical Spherical Deformation for Shape Correspondence.
We present novel spherical deformation for a landmark-free shape correspondence in a group-wise manner. In this work, we aim at both addressing template selection bias and minimizing registration distortion in a single framework. The proposed spherical deformation yields a non-rigid deformation field without referring to any particular spherical coordinate system. Specifically, we extend a rigid rotation represented by well-known Euler angles to general non-rigid local deformation via spatial-varying Euler angles. The proposed method employs spherical harmonics interpolation of the local displacements to simultaneously sol...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 7, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Automated Segmentation of the Left and Right Ventricles in 4D Cardiac SPAMM Images.
Authors: Montillo A, Metaxas D, Axel L Abstract In this paper we describe a completely automated volume-based method for the segmentation of the left and right ventricles in 4D tagged MR (SPAMM) images for quantitative cardiac analysis. We correct the background intensity variation in each volume caused by surface coils using a new scale-based fuzzy connectedness procedure. We apply 3D grayscale opening to the corrected data to create volumes containing only the blood filled regions. We threshold the volumes by minimizing region variance or by an adaptive statistical thresholding method. We isolate the ventricular ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - November 20, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Deep learning within a priori temporal feature spaces for large-scale dynamic MR image reconstruction: Application to 5-D cardiac MR Multitasking.
Authors: Chen Y, Shaw JL, Xie Y, Li D, Christodoulou AG Abstract High spatiotemporal resolution dynamic magnetic resonance imaging (MRI) is a powerful clinical tool for imaging moving structures as well as to reveal and quantify other physical and physiological dynamics. The low speed of MRI necessitates acceleration methods such as deep learning reconstruction from under-sampled data. However, the massive size of many dynamic MRI problems prevents deep learning networks from directly exploiting global temporal relationships. In this work, we show that by applying deep neural networks inside a priori calculated tem...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - November 16, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Active Appearance Model Induced Generative Adversarial Network for Controlled Data Augmentation.
Authors: Liu J, Shen C, Liu T, Aguilera N, Tam J Abstract Data augmentation is an important strategy for enlarging training datasets in deep learning-based medical image analysis. This is because large, annotated medical datasets are not only difficult and costly to generate, but also quickly become obsolete due to rapid advances in imaging technology. Image-to-image conditional generative adversarial networks (C-GAN) provide a potential solution for data augmentation. However, annotations used as inputs to C-GAN are typically based only on shape information, which can result in undesirable intensity distributions ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - November 9, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Automated Model-Based Segmentation of the Left and Right Ventricles in Tagged Cardiac MRI.
We describe an automated, model-based method to segment the left and right ventricles in 4D tagged MR. We fit 3D epicardial and endocardial surface models to ventricle features we extract from the image data. Excellent segmentation is achieved using novel methods that (1) initialize the models and (2) that compute 3D model forces from 2D tagged MR images. The 3D forces guide the models to patient-specific anatomy while the fit is regularized via internal deformation strain energy of a thin plate. Deformation continues until the forces equilibrate or vanish. Validation of the segmentations is performed quantitatively and qu...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - November 2, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Using Convolutional Neural Networks to Automatically Detect Eye-Blink Artifacts in Magnetoencephalography Without Resorting to Electrooculography.
Authors: Garg P, Davenport E, Murugesan G, Wagner B, Whitlow C, Maldjian J, Montillo A Abstract Magnetoencephelography (MEG) is a functional neuroimaging tool that records the magnetic fields induced by neuronal activity; however, signal from muscle activity often corrupts the data. Eye-blinks are one of the most common types of muscle artifact. They can be recorded by affixing eye proximal electrodes, as in electrooculography (EOG), however this complicates patient preparation and decreases comfort. Moreover, it can induce further muscular artifacts from facial twitching. We propose an EOG free, data driven approa...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 30, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Automatic classification of cochlear implant electrode cavity positioning.
Authors: Noble JH, Labadie RF, Dawant BM Abstract Cochlear Implants (CIs) restore hearing using an electrode array that is surgically implanted into the intra-cochlear cavities. Research has indicated that each electrode can lie in one of several cavities and that location is significantly associated with hearing outcomes. However, comprehensive analysis of this phenomenon has not been possible because the cavities are not directly visible in clinical CT images and because existing methods to estimate cavity location are not accurate enough, labor intensive, or their accuracy has not been validated. In this work, a...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 3, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Revealing Regional Associations of Cortical Folding Alterations with In Utero Ventricular Dilation Using Joint Spectral Embedding.
Authors: Benkarim OM, Sanroma G, Piella G, Rekik I, Hahner N, Eixarch E, Gonzélez Ballester MA, Shen D, Li G Abstract Fetal ventriculomegaly (VM) is a condition with dilation of one or both lateral ventricles, and is diagnosed as an atrial diameter larger than 10 mm. Evidence of altered cortical folding associated with VM has been shown in the literature. However, existing studies use a holistic approach (i.e., ventricle as a whole) based on diagnosis or ventricular volume, thus failing to reveal the spatially-heterogeneous association patterns between cortex and ventricle. To address this issue, we develop ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - July 3, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Registration-Free Infant Cortical Surface Parcellation using Deep Convolutional Neural Networks.
Authors: Wu Z, Li G, Li W, Shi F, Lin W, Gilmore JH, Shen D Abstract Automatic parcellation of infant cortical surfaces into anatomical regions of interest (ROIs) is of great importance in brain structural and functional analysis. Conventional cortical surface parcellation methods suffer from two main issues: 1) Cortical surface registration is needed for establishing the atlas-to-individual correspondences; 2) The mapping from cortical shape to the parcellation labels requires designing of specific hand-crafted features. To address these issues, in this paper, we propose a novel cortical surface parcellation metho...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - July 3, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Joint High-Order Multi-Task Feature Learning to Predict the Progression of Alzheimer's Disease.
Authors: Brand L, Wang H, Huang H, Risacher S, Saykin A, Shen L, ADNI Abstract Alzheimer's disease (AD) is a degenerative brain disease that affects millions of people around the world. As populations in the United States and worldwide age, the prevalence of Alzheimer's disease will only increase. In turn, the social and financial costs of AD will create a difficult environment for many families and caregivers across the globe. By combining genetic information, brain scans, and clinical data, gathered over time through the Alzheimer's Disease Neuroimaging Initiative (ADNI), we propose a new Joint High-Order Multi-M...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 12, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis.
Authors: Yan W, Zhang H, Sui J, Shen D Abstract Brain functional connectivity (FC) extracted from resting-state fMRI (RS-fMRI) has become a popular approach for disease diagnosis, where discriminating subjects with mild cognitive impairment (MCI) from normal controls (NC) is still one of the most challenging problems. Dynamic functional connectivity (dFC), consisting of time-varying spatiotemporal dynamics, may characterize "chronnectome" diagnostic information for improving MCI classification. However, most of the current dFC studies are based on detecting discrete major "brain status" via spat...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 12, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Exploratory Population Analysis with Unbalanced Optimal Transport.
Authors: Gerber S, Niethammer M, Styner M, Aylward S Abstract The plethora of data from neuroimaging studies provide a rich opportunity to discover effects and generate hypotheses through exploratory data analysis. Brain pathologies often manifest in changes in shape along with deterioration and alteration of brain matter, i.e., changes in mass. We propose a morphometry approach using unbalanced optimal transport that detects and localizes changes in mass and separates them from changes due to the location of mass. The approach generates images of mass allocation and mass transport cost for each subject in the popu...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 8, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Efficient Laplace Approximation for Bayesian Registration Uncertainty Quantification.
Authors: Wang J, Wells WM, Golland P, Zhang M Abstract This paper presents a novel approach to modeling the pos terior distribution in image registration that is computationally efficient for large deformation diffeomorphic metric mapping (LDDMM). We develop a Laplace approximation of Bayesian registration models entirely in a bandlimited space that fully describes the properties of diffeomorphic transformations. In contrast to current methods, we compute the inverse Hessian at the mode of the posterior distribution of diffeomorphisms directly in the low dimensional frequency domain. This dramatically reduces the c...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 29, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Low-Rank Representation for Multi-center Autism Spectrum Disorder Identification.
Authors: Wang M, Zhang D, Huang J, Shen D, Liu M Abstract Effective utilization of multi-center data for autism spectrum disorder (ASD) diagnosis recently has attracted increasing attention, since a large number of subjects from multiple centers are beneficial for investigating the pathological changes of ASD. To better utilize the multi-center data, various machine learning methods have been proposed. However, most previous studies do not consider the problem of data heterogeneity (e.g., caused by different scanning parameters and subject populations) among multi-center datasets, which may degrade the diagnosis pe...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 22, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Elastic Registration of Single Subject Task Based fMRI Signals.
Authors: Lee DS, Loureiro J, Narr KL, Woods RP, Joshi SH Abstract Single subject task-based fMRI analyses generally suffer from low detection sensitivity with parameter estimates from the general linear model (GLM) lying below the significance threshold especially for similar contrasts or conditions. In this paper, we present a shape-based approach for alignment of condition-specific time course activity for single subject task-based fMRI. Our approach extracts signals for each condition from the entire time course, constructs an unbiased average of those signals, and warps each signal to the mean. As the warping i...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 22, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis.
Authors: Kam TE, Zhang H, Shen D Abstract Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been considered as promising biomarkers for early Alzheimer's disease (AD) diagnosis, it is still challenging to perform individualized diagnosis, especially at the very early stage of preclinical stage of AD, i.e., early mild cognitive impairment (eMCI). Recently, convolutional neural networks (CNNs) show powerful ability in computer vision and image analysis applications, but there is still a gap for directly applying CNNs to rs-fMRI-ba...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 22, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Temporal Correlation Structure Learning for MCI Conversion Prediction.
Authors: Wang X, Cai W, Shen D, Huang H Abstract In Alzheimer's research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer's. How to distinguish MCI samples that finally convert to AD from those do not is an essential problem in the prevention and diagnosis of Alzheimer's. Traditional methods use various classification models to distinguish MCI converters from non-converters, while the performance is usually limited by the small number of available data. Moreover, previous methods only use the data at baseline time for training but ignore the longitudinal informa...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 22, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

A Novel Mixed Reality Navigation System for Laparoscopy Surgery.
Authors: Jayender J, Xavier B, King F, Hosny A, Black D, Pieper S, Tavakkoli A Abstract OBJECTIVE: To design and validate a novel mixed reality head-mounted display for intraoperative surgical navigation. DESIGN: A mixed reality navigation for laparoscopic surgery (MRNLS) system using a head mounted display (HMD) was developed to integrate the displays from a laparoscope, navigation system, and diagnostic imaging to provide context-specific information to the surgeon. Further, an immersive auditory feedback was also provided to the user. Sixteen surgeons were recruited to quantify the differential improvement i...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 19, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Locality Adaptive Multi-modality GANs for High-Quality PET Image Synthesis.
Authors: Wang Y, Zhou L, Wang L, Yu B, Zu C, Lalush DS, Lin W, Wu X, Zhou J, Shen D Abstract Positron emission topography (PET) has been substantially used in recent years. To minimize the potential health risks caused by the tracer radiation inherent to PET scans, it is of great interest to synthesize the high-quality full-dose PET image from the low-dose one to reduce the radiation exposure while maintaining the image quality. In this paper, we propose a locality adaptive multi-modality generative adversarial networks model (LA-GANs) to synthesize the full-dose PET image from both the low-dose one and the accompa...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 7, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps.
We present a novel multi-task convolutional neural network called Multi-task SonoEyeNet (M-SEN) that learns to generate clinically relevant visual attention maps using sonographer gaze tracking data on input ultrasound (US) video frames so as to assist standardized abdominal circumference (AC) plane detection. Our architecture consists of a generator and a discriminator, which are trained in an adversarial scheme. The generator learns sonographer attention on a given US video frame to predict the frame label (standardized AC plane / background). The discriminator further fine-tunes the predicted attention map by encouragin...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 17, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Dual-Domain Cascaded Regression for Synthesizing 7T from 3T MRI.
Authors: Zhang Y, Cheng JZ, Xiang L, Yap PT, Shen D Abstract Due to the high cost and low accessibility of 7T magnetic resonance imaging (MRI) scanners, we propose a novel dual-domain cascaded regression framework to synthesize 7T images from the routine 3T images. Our framework is composed of two parallel and interactive multi-stage regression streams, where one stream regresses on spatial domain and the other regresses on frequency domain. These two streams complement each other and enable the learning of complex mappings between 3T and 7T images. We evaluated the proposed framework on a set of 3T and 7T images b...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 10, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Patch-based Mapping of Transentorhinal Cortex with a Distributed Atlas.
Authors: Gahm JK, Tang Y, Shi Y Abstract The significance of the transentorhinal (TE) cortex has been well known for the early diagnosis of Alzheimer's disease (AD). However, precise mapping of the TE cortex for the detection of local changes in the region was not well established mostly due to significant geometric variations around TE. In this paper, we propose a novel framework for automated patch generation of the TE cortex, patch-based mapping, and construction of an atlas with a distributed network. We locate the TE cortex and extract a small patch surrounding the TE cortex from a cortical surface using a coa...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 10, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information.
Authors: Xiang L, Chen Y, Chang W, Zhan Y, Lin W, Wang Q, Shen D Abstract T1-weighted image (T1WI) and T2-weighted image (T2WI) are the two routinely acquired Magnetic Resonance Imaging (MRI) protocols that provide complementary information for diagnosis. However, the total acquisition time of ~10 min yields the image quality vulnerable to artifacts such as motion. To speed up MRI process, various algorithms have been proposed to reconstruct high quality images from under-sampled k-space data. These algorithms only employ the information of an individual protocol (e.g., T2WI). In this paper, we propose to combine c...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 26, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport.
We present here a novel visualization framework, GlymphVIS, which uses regularized optimal transport (OT) to study the flow behavior between time points at which the images are taken. Using this regularized OT app-roach, we can incorporate diffusion, handle noise, and accurately capture and visualize the time varying dynamics in GS transport. Moreover, we are able to reduce the registration mean-squared and infinity-norm error across time points by up to a factor of 5 as compared to the current state-of-the-art method. Our visualization pipeline yields flow patterns that align well with experts' current findings of the gly...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 26, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector.
Authors: Singla S, Gong M, Ravanbakhsh S, Sciurba F, Poczos B, Batmanghelich KN Abstract We propose an attention-based method that aggregates local image features to a subject-level representation for predicting disease severity. In contrast to classical deep learning that requires a fixed dimensional input, our method operates on a set of image patches; hence it can accommodate variable length input image without image resizing. The model learns a clinically interpretable subject-level representation that is reflective of the disease severity. Our model consists of three mutually dependent modules which regulate e...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 23, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Learning Generalizable Recurrent Neural Networks from Small Task-fMRI Datasets.
Authors: Dvornek NC, Yang D, Ventola P, Duncan JS Abstract Deep learning has become the new state-of-the-art for many problems in image analysis. However, large datasets are often required for such deep networks to learn effectively. This poses a difficult challenge for many medical image analysis problems in which only a small number of subjects are available, e.g., patients undergoing a new treatment. In this work, we propose a number of approaches for learning generalizable recurrent neural networks from smaller task-fMRI datasets: 1) a resampling method for ROI-based fMRI analysis to create augmented data; 2) i...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 17, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation.
Authors: Qi H, Collins S, Noble JA Abstract Accurate localization of structural abnormalities is a precursor for image-based prenatal assessment of adverse conditions. For clinical screening and diagnosis of abnormally invasive placenta (AIP), a life-threatening obstetric condition, qualitative and quantitative analysis of ultrasonic patterns correlated to placental lesions such as placental lacunae (PL) is challenging and time-consuming to perform even for experienced sonographers. There is a need for automated placental lesion localization that does not rely on expensive human annotations such as detailed manual ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 13, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Using the Anisotropic Laplace Equation to Compute Cortical Thickness.
We describe a novel method based on the anisotropic heat equation that explicitly accounts for the presence of partial tissue volumes to more accurately estimate cortical thickness. The anisotropic term uses gray matter fractions to incorporate partial tissue voxels into the thickness calculation, as demonstrated through simulations and experiments. We also show that the proposed method is robust to the effects of finite voxel resolution and blurring. In comparison to methods based on hard intensity thresholds, the heat equation based method yields results with in-vivo data that are more consistent with histological findin...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 10, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

rfDemons: Resting fMRI-based Cortical Surface Registration using the BrainSync Transform.
Authors: Joshi AA, Li J, Chong M, Akrami H, Leahy RM Abstract 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 connectivi...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 5, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear.
Authors: Wang J, Zhao Y, Noble JH, Dawant BM Abstract 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. As a pre-processing step, we also propose a band...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - January 31, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration.
Authors: Fan J, Cao X, Xue Z, Yap PT, Shen D Abstract 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 ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - January 12, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Efficient Groupwise Registration of MR Brain Images via Hierarchical Graph Set Shrinkage.
Authors: Dong P, Cao X, Yap PT, Shen D Abstract 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. Specifically, first, we divide...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 22, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Adversarial Domain Adaptation for Classification of Prostate Histopathology Whole-Slide Images.
Authors: Ren J, Hacihaliloglu I, Singer EA, Foran DJ, Qi X Abstract 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...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - November 23, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Craniomaxillofacial Bony Structures Segmentation from MRI with Deep-Supervision Adversarial Learning.
Authors: Zhao M, Wang L, Chen J, Nie D, Cong Y, Ahmad S, Ho A, Yuan P, Fung SH, Deng HH, Xia J, Shen D Abstract 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. Ther...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - November 20, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Volume-Based Analysis of 6-Month-Old Infant Brain MRI for Autism Biomarker Identification and Early Diagnosis.
Authors: Wang L, Li G, Shi F, Cao X, Lian C, Nie D, Liu M, Zhang H, Li G, Wu Z, Lin W, Shen D Abstract 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. In t...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - November 16, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Identification of Multi-scale Hierarchical Brain Functional Networks Using Deep Matrix Factorization.
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 meas...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 24, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

A Tetrahedron-based Heat Flux Signature for Cortical Thickness Morphometry Analysis.
Authors: Fan Y, Wang G, Lepore N, Wang Y Abstract 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 tetrahedr...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 20, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI.
In this study, we develop a deep learning based framework for adaptively detecting temporally dynamic functional state transitions in a data-driven way without any explicit modeling assumptions, by leveraging recent advances in recurrent neural networks (RNNs) for sequence modeling. Particularly, we solve this problem in an anomaly detection framework with an assumption that the functional profile of one single time point could be reliably predicted based on its preceding profiles within a stable functional state, while large prediction errors would occur around change points of functional states. We evaluate the proposed ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 17, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks.
In this study, we develop a deep learning based framework for brain decoding by leveraging recent advances in sequence modeling using long short-term memory (LSTM) recurrent neural networks (RNNs). Particularly, functional profiles extracted from task functional imaging data based on their corresponding subject-specific intrinsic functional networks are used as features to build brain decoding models, and LSTM RNNs are adopted to learn decoding mappings between functional profiles and brain states. We evaluate the proposed method using task fMRI data from the HCP dataset, and experimental results have demonstrated that the...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 17, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation.
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. In comparison, state-of-the art adversarial networks trained without our tumor-aware ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 10, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Multimodal Fusion of Brain Networks with Longitudinal Couplings.
Authors: Zhang W, Shu K, Wang S, Liu H, Wang Y Abstract 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...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - October 4, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Joint Sparse and Low-Rank Regularized MultiTask Multi-Linear Regression for Prediction of Infant Brain Development with Incomplete Data.
Authors: Adeli E, Meng Y, Li G, Lin W, Shen D Abstract 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. We treat this as a multi-dime...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 31, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Novel Single and Multiple Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes.
Authors: Cheng J, Shen D, Yap PT, Basser PJ Abstract A good data sampling scheme is important for diffusion MRI acquisition and reconstruction. Diffusion Weighted Imaging (DWI) data is normally acquired on single or multiple shells in q-space. The samples in different shells are typically distributed uniformly, because they should be invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI by Jones et al., was recently generalized to the multi-shell case, called gener...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 15, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Diffusion Compartmentalization Using Response Function Groups with Cardinality Penalization.
Authors: Yap PT, Zhang Y, Shen D Abstract Spherical deconvolution (SD) of the white matter (WM) diffusion-attenuated signal with a fiber signal response function has been shown to yield high-quality estimates of fiber orientation distribution functions (FODFs). However, an inherent limitation of this approach is that the response function (RF) is often fixed and assumed to be spatially invariant. This has been reported to result in spurious FODF peaks as the discrepancy of the RF with the data increases. In this paper, we propose to utilize response function groups (RFGs) for robust compartmentalization of diffusio...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 15, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Iterative Subspace Screening for Rapid Sparse Estimation of Brain Tissue Microstructural Properties.
Authors: Yap PT, Zhang Y, Shen D Abstract Diffusion magnetic resonance imaging (DMRI) is a powerful imaging modality due to its unique ability to extract microstructural information by utilizing restricted diffusion to probe compartments that are much smaller than the voxel size. Quite commonly, a mixture of models is fitted to the data to infer microstructural properties based on the estimated parameters. The fitting process is often non-linear and computationally very intensive. Recent work by Daducci et al. has shown that speed improvement of several orders of magnitude can be achieved by linearizing and recasti...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 15, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Hierarchical Reconstruction of 7T-like Images from 3T MRI Using Multi-level CCA and Group Sparsity.
Authors: Bahrami K, Shi F, Zong X, Shin HW, An H, Shen D Abstract Advancements in 7T MR imaging bring higher spatial resolution and clearer tissue contrast, in comparison to the conventional 3T and 1.5T MR scanners. However, 7T MRI scanners are less accessible at the current stage due to higher costs. Through analyzing the appearances of 7T images, we could improve both the resolution and quality of 3T images by properly mapping them to 7T-like images; thus, promoting more accurate post-processing tasks, such as segmentation. To achieve this method based on an unique dataset acquired both 3T and 7T images from same...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 15, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Semi-supervised Hierarchical Multimodal Feature and Sample Selection for Alzheimer's Disease Diagnosis.
Authors: An L, Adeli E, Liu M, Zhang J, Shen D Abstract Alzheimer's disease (AD) is a progressive neurodegenerative disease that impairs a patient's memory and other important mental functions. In this paper, we leverage the mutually informative and complementary features from both structural magnetic resonance imaging (MRI) and single nucleotide polymorphism (SNP) for improving the diagnosis. Due to the feature redundancy and sample outliers, direct use of all training data may lead to suboptimal performance in classification. In addition, as redundant features are involved, the most discriminative feature subset ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 15, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Accurate Correspondence of Cone Photoreceptor Neurons in the Human Eye Using Graph Matching Applied to Longitudinal Adaptive Optics Images.
Authors: Liu J, Jung H, Tam J Abstract 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) r...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - August 8, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Brain Tissue Segmentation Based on Diffusion MRI Using ℓ0 Sparse-Group Representation Classification.
We present a method for automated brain tissue segmentation based on diffusion MRI. This provides information that is complementary to structural MRI and facilitates fusion of information between the two imaging modalities. Unlike existing segmentation approaches that are based on diffusion tensor imaging (DTI), our method explicitly models the coexistence of various diffusion compartments within each voxel owing to different tissue types and different fiber orientations. This results in improved segmentation in regions with white matter crossings and in regions susceptible to partial volume effects. For each voxel, we tea...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - July 26, 2018 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research