Autism Classification Using Topological Features and Deep Learning: A Cautionary Tale.
Authors: Rathore A, Palande S, Anderson JS, Zielinski BA, Fletcher PT, Wang B Abstract The identification of autistic individuals using resting state functional connectivity networks can provide an objective diagnostic method for autism spectrum disorder (ASD). The present state-of-the-art machine learning model using deep learning has a classification accuracy of 70.2% on the ABIDE (Autism Brain Imaging Data Exchange) data set. In this paper, we explore the utility of topological features in the classification of ASD versus typically developing control subjects. These topological features have been shown to provid...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - July 31, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Variational AutoEncoder For Regression: Application to Brain Aging Analysis.
Authors: Zhao Q, Adeli E, Honnorat N, Leng T, Pohl KM Abstract While unsupervised variational autoencoders (VAE) have become a powerful tool in neuroimage analysis, their application to supervised learning is under-explored. We aim to close this gap by proposing a unified probabilistic model for learning the latent space of imaging data and performing supervised regression. Based on recent advances in learning disentangled representations, the novel generative process explicitly models the conditional distribution of latent representations with respect to the regression target variable. Performing a variational inf...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - July 28, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Analysis of Morphological Changes of Lamina Cribrosa Under Acute Intraocular Pressure Change.
Authors: Ravier M, Hong S, Girot C, Ishikawa H, Tauber J, Wollstein G, Schuman J, Fishbaugh J, Gerig G Abstract Glaucoma is the second leading cause of blindness world-wide. Despite active research efforts driven by the importance of diagnosis and treatment of the optic degenerative neuropathy, the relationship between structural and functional changes along the glaucomateous evolution are still not clearly understood. Dynamic changes of the lamina cribrosa (LC) in the presence of intraocular pressure (IOP) were suggested to play a significant role in optic nerve damage, which motivates the proposed research to exp...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - July 15, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions.
We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1 million labeled fiber samples (54 anatomical tracts are included). To discriminate between fibers from different tracts, we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a CNN tract classification model based on FiberMap and obtain a high tract classificatio...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 21, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Bronchial Cartilage Assessment with Model-Based GAN Regressor.
Authors: Nardelli P, Washko GR, San José Estépar R Abstract In the last two decades, several methods for airway segmentation from chest CT images have been proposed. The following natural step is the development of a tool to accurately assess the morphology of the bronchial system in all its aspects to help physicians better diagnosis and prognosis complex pulmonary diseases such as COPD, chronic bronchitis and bronchiectasis. Traditional methods for the assessment of airway morphology usually focus on lumen and wall thickness and are often limited due to resolution and artifacts of the CT image. Airw...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 5, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators.
Authors: Egger B, Schirmer MD, Dubost F, Nardin MJ, Rost NS, Golland P Abstract We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to separate the joint dependency structure from the marginal distributions of variables of interest. This separation provides flexibility on the assumptions made during the modeling process. The proposed method can handle binary, discrete, ordinal and continuous variables. We demonstrate a simple and efficient way to include binary, discr...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 5, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network.
Authors: Xu J, Zhang M, Turk EA, Zhang L, Grant E, Ying K, Golland P, Adalsteinsson E Abstract The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion. Motion of the fetus, which is unpredictable and rapid on the scale of conventional imaging times, limits the set of viable acquisition techniques to single-shot imaging with severe compromises in signal-to-noise ratio and diagnostic contrast, and frequently results in unacceptable image quality. Surprisingly little is known about the characteristics of fetal motion during MRI and here we pr...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 5, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Linear Time Invariant Model based Motion Correction (LiMo-MoCo) of Dynamic Radial Contrast Enhanced MRI.
We present a bulk motion detection and a linear time invariant (LTI) model-based motion correction approach for DCE-MRI alignment that leverages the temporal dynamics of the DCE data at each voxel. We evaluate our approach on 10 newborn patients that underwent DCE imaging without sedation. For each patient, we reconstructed the sequence of DCE images, detected and removed the volumes corrupted by motion using a self navigation approach, aligned the sequence using our approach and fitted the TK model to compute GFR. The results show that our approach correctly aligned all volumes and improved the TK model fit and, on averag...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - June 4, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Statistical Framework for the Definition of Emphysema in CT Scans: Beyond Density Mask.
Authors: Vegas-Sánchez-Ferrero G, José Estépar RS Abstract Lung parenchyma destruction (emphysema) is a major factor in the description of Chronic Obstructive Pulmonary Disease (COPD) and its prognosis. It is defined as an abnormal enlargement of air spaces distal to the terminal bronchioles and the destruction of alveolar walls. In CT imaging, the presence of emphysema is observed by a local decrease of the lung density and the diagnose is usually set as more than 5% of the lung below -950 HU, the so-called emphysema density mask. There is still debate, however, about the definition of this pe...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 31, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Targeting Precision with Data Augmented Samples in Deep Learning.
We present two different applications of DL (regression and segmentation) to demonstrate the strength of the proposed strategy. We think that this work will pave the way to a explicit use of data augmentation within the loss function that helps the network to be invariant to small variations of the same input samples, a characteristic that is always required to every application in the medical imaging field. PMID: 32455347 [PubMed] (Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention)
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 28, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Disease Knowledge Transfer across Neurodegenerative Diseases.
Authors: Marinescu RV, Lorenzi M, Blumberg SB, Young AL, Planell-Morell P, Oxtoby NP, Eshaghi A, Yong KX, Crutch SJ, Golland P, Alexander DC Abstract We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits bio...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 22, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation.
Authors: Dalca AV, Yu E, Golland P, Fischl B, Sabuncu MR, Iglesias JE Abstract Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensit...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 22, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Placental Flattening via Volumetric Parameterization.
We present a volumetric mesh-based algorithm for flattening the placenta to a canonical template to enable effective visualization of local anatomy and function. Monitoring placental function in vivo promises to support pregnancy assessment and to improve care outcomes. We aim to alleviate visualization and interpretation challenges presented by the shape of the placenta when it is attached to the curved uterine wall. To do so, we flatten the volumetric mesh that captures placental shape to resemble the well-studied ex vivo shape. We formulate our method as a map from the in vivo shape to a flattened template that minimize...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 22, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Integrating cross-modality hallucinated MRI with CT to aid mediastinal lung tumor segmentation.
Authors: Jue J, Jason H, Neelam T, Andreas R, Sean BL, Joseph DO, Harini V Abstract Lung tumors, especially those located close to or surrounded by soft tissues like the mediastinum, are difficult to segment due to the low soft tissue contrast on computed tomography images. Magnetic resonance images contain superior soft-tissue contrast information that can be leveraged if both modalities were available for training. Therefore, we developed a cross-modality educed learning approach where MR information that is educed from CT is used to hallucinate MRI and improve CT segmentation. Our approach, called cross-modality...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 19, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

A Natural Language Interface for Dissemination of Reproducible Biomedical Data Science.
Authors: John RJL, Patel JM, Alexander AL, Singh V, Adluru N Abstract Computational tools in the form of software packages are burgeoning in the field of medical imaging and biomedical research. These tools enable biomedical researchers to analyze a variety of data using modern machine learning and statistical analysis techniques. While these publicly available software packages are a great step towards a multiplicative increase in the biomedical research productivity, there are still many open issues related to validation and reproducibility of the results. A key gap is that while scientists can validate domain in...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 18, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Hierarchical Parcellation of the Cerebellum.
Authors: Han S, Carass A, Prince JL Abstract Parcellation of the cerebellum in an MR image has been used to study regional associations with both motion and cognitive functions. Despite the fact that the division of the cerebellum is defined hierarchically-i.e., the cerebellum can be divided into lobes and the lobes can be further divided into lobules-previous automatic methods to parcellate the cerebellum do not utilize this information. In this work, we propose a method based on convolutional neural networks (CNNs) to explicitly incorporate the hierarchical organization of the cerebellum. The network is construct...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 15, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Mixed-Supervised Dual-Network for Medical Image Segmentation.
Authors: Wang D, Li M, Ben-Shlomo N, Corrales CE, Cheng Y, Zhang T, Jayender J Abstract Deep learning based medical image segmentation models usually require large datasets with high-quality dense segmentations to train, which are very time-consuming and expensive to prepare. One way to tackle this difficulty is using the mixed-supervised learning framework, where only a part of data is densely annotated with segmentation label and the rest is weakly labeled with bounding boxes. The model is trained jointly in a multi-task learning setting. In this paper, we propose Mixed-Supervised Dual-Network (MSDN), a novel arc...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 13, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Real-Time Surface Deformation Recovery from Stereo Videos.
Authors: Zhou H, Jagadeesan J Abstract Tissue deformation during the surgery may significantly decrease the accuracy of surgical navigation systems. In this paper, we propose an approach to estimate the deformation of tissue surface from stereo videos in real-time, which is capable of handling occlusion, smooth surface and fast deformation. We first use a stereo matching method to extract depth information from stereo video frames and generate the tissue template, and then estimate the deformation of the obtained template by minimizing ICP, ORB feature matching and as-rigid-as-possible (ARAP) costs. The main novelt...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 12, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Unsupervised Domain Adaptation via Disentangled Representations: Application to Cross-Modality Liver Segmentation.
Authors: Yang J, Dvornek NC, Zhang F, Chapiro J, Lin M, Duncan JS Abstract A deep learning model trained on some labeled data from a certain source domain generally performs poorly on data from different target domains due to domain shifts. Unsupervised domain adaptation methods address this problem by alleviating the domain shift between the labeled source data and the unlabeled target data. In this work, we achieve cross-modality domain adaptation, i.e. between CT and MRI images, via disentangled representations. Compared to learning a one-to-one mapping as the state-of-art CycleGAN, our model recovers a manyto-m...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - May 9, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Invertible Network for Classification and Biomarker Selection for ASD.
Authors: Zhuang J, Dvornek NC, Li X, Ventola P, Duncan JS Abstract Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification task of ASD using fMRI data. However, due to the black-box nature of most deep learning models, it's hard to perform biomarker selection and interpret model decisions. The recently proposed invertible networks can accurately reconstruct the input from its output, and have the potential to unravel the black-box representation. Therefore, we propose a novel method to classify A...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - April 12, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties.
Authors: Wang F, Lian C, Wu Z, Wang L, Lin W, Gilmore JH, Shen D, Li G Abstract The human brain develops dynamically and regionally heterogeneously during the first two postnatal years. Cortical developmental regionalization, i.e., the landscape of cortical heterogeneity in development, reflects the organization of underlying microstructures, which are closely related to the functional principles of the cortex. Therefore, prospecting early cortical developmental regionalization can provide neurobiologically meaningful units for precise region localization, which will advance our understanding on brain development i...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 20, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Deep Granular Feature-Label Distribution Learning for Neuroimaging-based Infant Age Prediction.
Authors: Hu D, Zhang H, Wu Z, Lin W, Li G, Shen D, UNC/UMN Baby Connectome Project Consortium Abstract Neuroimaging-based infant age prediction is important for brain development analysis but often suffers insufficient data. To address this challenge, we introduce label distribution learning (LDL), a popular machine learning paradigm focusing on the small sample problem, for infant age prediction. As directly applying LDL yields dramatically increased number of day-to-day age labels and also extremely scarce data describing each label, we propose a new strategy, called granular label distribution (GLD). Particularl...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 19, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

RCA-U-Net: Residual Channel Attention U-Net for Fast Tissue Quantification in Magnetic Resonance Fingerprinting.
Authors: Fang Z, Chen Y, Nie D, Lin W, Shen D Abstract Magnetic resonance fingerprinting (MRF) is a relatively new imaging framework which allows rapid and simultaneous quantification of multiple tissue properties, such as T1 and T2 relaxation times, in one acquisition. To accelerate the data sampling in MRF, a variety of methods have been proposed to extract tissue properties from highly accelerated MRF signals. While these methods have demonstrated promising results, further improvement in the accuracy, especially for T2 quantification, is needed. In this paper, we present a novel deep learning approach, namely r...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 13, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Reconstructing High-Quality Diffusion MRI Data from Orthogonal Slice-Undersampled Data Using Graph Convolutional Neural Networks.
Authors: Hong Y, Chen G, Yap PT, Shen D Abstract Diffusion MRI (dMRI), while powerful for the characterization of tissue microstructure, suffers from long acquisition times. In this paper, we propose a super-resolution (SR) reconstruction method based on orthogonal slice-undersampling for accelerated dMRI acquisition. Instead of scanning full diffusion-weighted (DW) image volumes, only a subsample of equally-spaced slices need to be acquired. We show that complementary information from DW volumes corresponding to different diffusion wave-vectors can be harnessed using graph convolutional neural networks for reconst...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 13, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

A Matched Filter Decomposition of fMRI into Resting and Task Components.
We present a closed-form expression for the windowed synchronization transform that is used by the matched filter. We demonstrate performance of this procedure in application to motor task and language task fMRI data. We show qualitatively and quantitatively that by removing the resting activity, we are able to identify task activated regions in the brain more clearly. Additionally, we show improved prediction accuracy in multivariate pattern analysis when using the matched filtered fMRI data. PMID: 32161932 [PubMed] (Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention)
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 13, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Wavelet-based Semi-supervised Adversarial Learning for Synthesizing Realistic 7T from 3T MRI.
Authors: Qu L, Wang S, Yap PT, Shen D Abstract Ultra-high field 7T magnetic resonance imaging (MRI) scanners produce images with exceptional anatomical details, which can facilitate diagnosis and prognosis. However, 7T MRI scanners are often cost prohibitive and hence inaccessible. In this paper, we propose a novel wavelet-based semi-supervised adversarial learning framework to synthesize 7T MR images from their 3T counterparts. Unlike most learning methods that rely on supervision requiring a significant amount of 3T-7T paired data, our method applies a semi-supervised learning mechanism to leverage unpaired 3T an...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 13, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brain.
Authors: Ahmad S, Wu Z, Li G, Wang L, Lin W, Yap PT, Shen D, UNC/UMN Baby Connectome Project Consortium Abstract Infant brain atlases are essential for characterizing structural changes in the developing brain. Volumetric and cortical atlases are typically constructed independently, potentially causing discrepancies between tissue boundaries and cortical surfaces. In this paper, we present a method for surface-volume consistent construction of longitudinal brain atlases of infants from 2 weeks to 12 months of age. We first construct the 12-month atlas via groupwise surface-constrained volumetric registration. The l...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 6, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Intrinsic Patch-Based Cortical Anatomical Parcellation Using Graph Convolutional Neural Network on Surface Manifold.
Authors: Wu Z, Zhao F, Xia J, Wang L, Lin W, Gilmore JH, Li G, Shen D Abstract Automatic parcellation of cortical surfaces into anatomically meaningful regions of interest (ROIs) is of great importance in brain analysis. Due to the complex shape of the convoluted cerebral cortex, conventional methods generally require three steps to obtain the parcellations. First, the original cortical surface is iteratively inflated and mapped onto a spherical surface with minimal metric distortion, for providing a simpler shape for analysis. Then, a registration or learning-based labeling method is adopted to parcellate ROIs on ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 6, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Harmonization of Infant Cortical Thickness Using Surface-to-Surface Cycle-Consistent Adversarial Networks.
Authors: Zhao F, Wu Z, Wang L, Lin W, Xia S, Shen D, Li G, UNC/UMN Baby Connectome Project Consortium Abstract Increasing multi-site infant neuroimaging datasets are facilitating the research on understanding early brain development with larger sample size and bigger statistical power. However, a joint analysis of cortical properties (e.g., cortical thickness) is unavoidably facing the problem of non-biological variance introduced by differences in MRI scanners. To address this issue, in this paper, we propose cycle-consistent adversarial networks based on spherical cortical surface to harmonize cortical thickness ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - March 6, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Adaptive Sparsity Regularization Based Collaborative Clustering for Cancer Prognosis.
Authors: Liu H, Li H, Li Y, Yin S, Boimel P, Janopaul-Naylor J, Zhong H, Xiao Y, Ben-Josef E, Fan Y Abstract Radiomic approaches have achieved promising performance in prediction of clinical outcomes of cancer patients. Particularly, feature dimensionality reduction plays an important role in radiomic studies. However, conventional feature dimensionality reduction techniques are not equipped to suppress data noise or utilize latent supervision information of patient data under study (e.g. difference in patients) for learning discriminative low dimensional representations. To achieve feature dimensionality reduction...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 26, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Robust Cochlear Modiolar Axis Detection in CT.
Authors: Wimmer W, Vandersteen C, Guevara N, Caversaccio M, Delingette H Abstract The cochlea, the auditory part of the inner ear, is a spiral-shaped organ with large morphological variability. An individualized assessment of its shape is essential for clinical applications related to tonotopy and cochlear implantation. To unambiguously reference morphological parameters, reliable recognition of the cochlear modiolar axis in computed tomography (CT) images is required. The conventional method introduces measurement uncertainties, as it is based on manually selected and difficult to identify landmarks. Herein, we pr...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - February 2, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Captioning Ultrasound Images Automatically.
We describe an automatic natural language processing (NLP)-based image captioning method to describe fetal ultrasound video content by modelling the vocabulary commonly used by sonographers and sonologists. The generated captions are similar to the words spoken by a sonographer when describing the scan experience in terms of visual content and performed scanning actions. Using full-length second-trimester fetal ultrasound videos and text derived from accompanying expert voice-over audio recordings, we train deep learning models consisting of convolutional neural networks and recurrent neural networks in merged configuratio...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - January 26, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Efficient Ultrasound Image Analysis Models with Sonographer Gaze Assisted Distillation.
Authors: Patra A, Cai Y, Chatelain P, Sharma H, Drukker L, Papageorghiou A, Noble JA Abstract Recent automated medical image analysis methods have attained state-of-the-art performance but have relied on memory and compute-intensive deep learning models. Reducing model size without significant loss in performance metrics is crucial for time and memory-efficient automated image-based decision-making. Traditional deep learning based image analysis only uses expert knowledge in the form of manual annotations. Recently, there has been interest in introducing other forms of expert knowledge into deep learning architectu...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - January 18, 2020 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

A New Approach of Predicting Facial Changes following Orthognathic Surgery using Realistic Lip Sliding Effect.
Authors: Kim D, Kuang T, Rodrigues YL, Gateno J, Shen SGF, Wang X, Deng H, Yuan P, Alfi DM, Liebschner MAK, Xia JJ Abstract Accurate prediction of facial soft-tissue changes following orthognathic surgery is crucial for improving surgical outcome. However, the accuracy of current prediction methods still requires further improvement in clinically critical regions, especially the lips. We develop a novel incremental simulation approach using finite element method (FEM) with realistic lip sliding effect to improve the prediction accuracy in the area around the lips. First, lip-detailed patient-specific FE mesh is gen...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 31, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Fully Convolutional Boundary Regression for Retina OCT Segmentation.
Authors: He Y, Carass A, Liu Y, Jedynak BM, Solomon SD, Saidha S, Calabresi PA, Prince JL Abstract A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer surfaces, with correct hierarchy (topology) are desired for monitoring disease progression. State-of-the-art methods use a trained classifier to label each pixel into background, layer, or surface pixels. The final step of extracting the desired smooth surfaces with correct topology are mostly performed by graph methods (e.g. shortest path, grap...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 21, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

An Automatic Approach to Reestablish Final Dental Occlusion for 1-Piece Maxillary Orthognathic Surgery.
Authors: Deng H, Yuan P, Wong S, Gateno J, Garrett FA, Ellis RK, English JD, Jacob HB, Kim D, Xia JJ Abstract Accurately establishing a desired final dental occlusion of the upper and lower teeth is a critical step in orthognathic surgical planning. Traditionally, the final occlusion is established by hand-articulating the stone dental models. However, this process is inappropriate to digitally plan the orthognathic surgery using computer-aided surgical simulation. To date, there is no effective method of digitally establishing final occlusion. We propose a 3-stage approach to digitally and automatically establish ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 19, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Estimating Reference Bony Shape Model for Personalized Surgical Reconstruction of Posttraumatic Facial Defects.
Authors: Xiao D, Wang L, Deng H, Thung KH, Zhu J, Yuan P, Rodrigues YL, Perez L, Crecelius CE, Gateno J, Kuang T, Shen SGF, Kim D, Alfi DM, Yap PT, Xia JJ, Shen D Abstract In this paper, we introduce a method for estimating patient-specific reference bony shape models for planning of reconstructive surgery for patients with acquired craniomaxillofacial (CMF) trauma. We propose an automatic bony shape estimation framework using pre-traumatic portrait photographs and post-traumatic head computed tomography (CT) scans. A 3D facial surface is first reconstructed from the patient's pre-traumatic photographs. An initial ...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 17, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

Adversarial Domain Adaptation and Pseudo-Labeling for Cross-Modality Microscopy Image Quantification.
Authors: Xing F, Bennett T, Ghosh D Abstract Cell or nucleus quantification has recently achieved state-of-the-art performance by using convolutional neural networks (CNNs). In general, training CNNs requires a large amount of annotated microscopy image data, which is prohibitively expensive or even impossible to obtain in some applications. Additionally, when applying a deep supervised model to new datasets, it is common to annotate individual cells in those target datasets for model re-training or fine-tuning, leading to low-throughput image analysis. In this paperSSS, we propose a novel adversarial domain adapta...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - December 13, 2019 Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research

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