Using deep neural networks for radiogenomic analysis.
USING DEEP NEURAL NETWORKS FOR RADIOGENOMIC ANALYSIS. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1529-1533 Authors: Smedley NF, Hsu W Abstract Radiogenomic studies have suggested that biological heterogeneity of tumors is reflected radiographically through visible features on magnetic resonance (MR) images. We apply deep learning techniques to map between tumor gene expression profiles and tumor morphology in pre-operative MR studies of glioblastoma patients. A deep autoencoder was trained on 528 patients, each with 12,042 gene expressions. Then, the autoencoder's weights were used to initialize ...
Source: Proceedings - International Symposium on Biomedical Imaging - August 11, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Brain age prediction based on resting-state functional connectivity patterns using convolutional neural networks.
In this study, we develop a deep learning method to use convolutional neural networks (CNNs) to learn informative features from the fine-grained whole brain FC measures for the brain age prediction. Experimental results on a large dataset of resting-state fMRI demonstrate that the deep learning model with fine-grained FC measures could better predict the brain age. PMID: 30079125 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - August 8, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation.
INTEGRATING SEMI-SUPERVISED LABEL PROPAGATION AND RANDOM FORESTS FOR MULTI-ATLAS BASED HIPPOCAMPUS SEGMENTATION. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:154-157 Authors: Zheng Q, Fan Y Abstract A novel multi-atlas based image segmentation method is proposed by integrating a semi-supervised label propagation method and a supervised random forests method in a pattern recognition based label fusion framework. The semi-supervised label propagation method takes into consideration local and global image appearance of images to be segmented and segments the images by propagating reliable segmentation...
Source: Proceedings - International Symposium on Biomedical Imaging - August 8, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Non-rigid image registration using self-supervised fully convolutional networks without training data.
NON-RIGID IMAGE REGISTRATION USING SELF-SUPERVISED FULLY CONVOLUTIONAL NETWORKS WITHOUT TRAINING DATA. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1075-1078 Authors: Li H, Fan Y Abstract A novel non-rigid image registration algorithm is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered in a self-supervised learning framework. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method d...
Source: Proceedings - International Symposium on Biomedical Imaging - August 8, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Transfer learning for diagnosis of congenital abnormalities of the kidney and urinary tract in children based on ultrasound imaging data.
In this study, we propose a transfer learning-based method to extract imaging features from US kidney images in order to improve the CAKUT diagnosis in children. Particularly, a pre-trained deep learning model (imagenet-caffe-alex) is adopted for transfer learning-based feature extraction from 3-channel feature maps computed from US images, including original images, gradient features, and distanced transform features. Support vector machine classifiers are then built upon different sets of features, including the transfer learning features, conventional imaging features, and their combination. Experimental results have de...
Source: Proceedings - International Symposium on Biomedical Imaging - August 8, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Enigma military brain injury: a coordinated meta-analysis of diffusion mri from multiple cohorts.
We present preliminary dMRI results from the ENIGMA (Enhancing Neuroimaging Genetics through Meta-Analysis) military brain injury working group. We found higher fractional anisotropy (FA) in participants with a history of TBI. Understanding the injury and recovery process, along with factors that influence these, will lead to improved diagnosis and treatment. PMID: 30034577 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - July 25, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Multi-task sparse screening for predicting future clinical scores using longitudinal cortical thickness measures.
MULTI-TASK SPARSE SCREENING FOR PREDICTING FUTURE CLINICAL SCORES USING LONGITUDINAL CORTICAL THICKNESS MEASURES. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1406-1410 Authors: Zhang J, Tu Y, Li Q, Caselli RJ, Thompson PM, Ye J, Wang Y Abstract Cortical thickness estimation performed in-vivo via magnetic resonance imaging (MRI) is an effective measure of brain atrophy in preclinical individuals at high risk for Alzheimer's disease (AD). However, the high dimensionality of individual cortical thickness data coupled with small population samples make it challenging to perform cortical thickness feat...
Source: Proceedings - International Symposium on Biomedical Imaging - July 21, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Estimating shape correspondence for populations of objects with complex topology.
ESTIMATING SHAPE CORRESPONDENCE FOR POPULATIONS OF OBJECTS WITH COMPLEX TOPOLOGY. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1010-1013 Authors: Fishbaugh J, Pascal L, Fischer L, Nguyen T, Boen C, Goncalves J, Gerig G, Paniagua B Abstract Statistical shape analysis captures the geometric properties of a given set of shapes, obtained from medical images, by means of statistical methods. Orthognathic surgery is a type of craniofacial surgery that is aimed at correcting severe skeletal deformities in the mandible and maxilla. Methods assuming spherical topology cannot represent the class of anatomica...
Source: Proceedings - International Symposium on Biomedical Imaging - July 6, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

4d continuous medial representation by geodesic shape regression.
4D CONTINUOUS MEDIAL REPRESENTATION BY GEODESIC SHAPE REGRESSION. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1014-1017 Authors: Hong S, Fishbaugh J, Gerig G Abstract Longitudinal shape analysis has shown great potential to model anatomical processes from baseline to follow-up observations. Shape regression estimates a continuous trajectory of time-discrete anatomical shapes to quantify temporal changes. The need for shape alignment and point-to-point correspondences represent limitations of current shape analysis methodologies, and present significant challenges in shape evaluation. We propose a ...
Source: Proceedings - International Symposium on Biomedical Imaging - July 6, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Linear convolution model of fetal circulation for hemodynamic responses to maternal hyperoxia using in utero functional mri.
LINEAR CONVOLUTION MODEL OF FETAL CIRCULATION FOR HEMODYNAMIC RESPONSES TO MATERNAL HYPEROXIA USING IN UTERO FUNCTIONAL MRI. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1284-1287 Authors: You W, Xu F, Limperopoulos C Abstract Functional MRI studies have started the hemodynamic responses of the placenta and fetal brain using maternal hyperoxia. While most studies have focused on analyzing the changes in magnitude of fMRI signals, few studies have analyzed the latency and duration of responses to hyperoxia. This paper proposes a linear convolution model of fetal circulation where a chain of response...
Source: Proceedings - International Symposium on Biomedical Imaging - June 15, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Scoliosis screening and monitoring using self contained ultrasound and neural networks.
SCOLIOSIS SCREENING AND MONITORING USING SELF CONTAINED ULTRASOUND AND NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1500-1503 Authors: Greer H, Gerber S, Niethammer M, Kwitt R, McCormick M, Chittajallu D, Siekierski N, Oetgen M, Cleary K, Aylward S Abstract We aim to diagnose scoliosis using a self contained ultrasound device that does not require significant training to operate. The device knows its angle relative to vertical using an embedded inertial measurement unit, and it estimates its angle relative to a vertebrae using a neural network analysis of its ultrasound images. The...
Source: Proceedings - International Symposium on Biomedical Imaging - June 15, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Ultrasound spectroscopy.
ULTRASOUND SPECTROSCOPY. Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:1013-1016 Authors: Aylward SR, McCormick M, Kang HJ, Razzaque S, Kwitt R, Niethammer M Abstract We introduce the concept of "Ultrasound Spectroscopy". The premise of ultrasound spectroscopy is that by acquiring ultrasound RF data at multiple power and frequency settings, a rich set of features can be extracted from that RF data and used to characterize the underlying tissues. This is beneficial for a variety of problems, such as accurate tissue classification, application-specific image generation, and numerous other qu...
Source: Proceedings - International Symposium on Biomedical Imaging - June 14, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Efficient registration of pathological images: a joint pca/image-reconstruction approach.
EFFICIENT REGISTRATION OF PATHOLOGICAL IMAGES: A JOINT PCA/IMAGE-RECONSTRUCTION APPROACH. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:10-14 Authors: Han X, Yang X, Aylward S, Kwitt R, Niethammer M Abstract Registration involving one or more images containing pathologies is challenging, as standard image similarity measures and spatial transforms cannot account for common changes due to pathologies. Low-rank/Sparse (LRS) decomposition removes pathologies prior to registration; however, LRS is memory-demanding and slow, which limits its use on larger data sets. Additionally, LRS blurs normal tissue ...
Source: Proceedings - International Symposium on Biomedical Imaging - June 14, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Compressive Sensing Based Q-Space Resampling for Handling Fast Bulk Motion in Hardi Acquisitions.
Authors: Elhabian S, Vachet C, Piven J, for IBIS, Styner M, Gerig G Abstract Diffusion-weighted (DW) MRI has become a widely adopted imaging modality to reveal the underlying brain connectivity. Long acquisition times and/or non-cooperative patients increase the chances of motion-related artifacts. Whereas slow bulk motion results in inter-gradient misalignment which can be handled via retrospective motion correction algorithms, fast bulk motion usually affects data during the application of a single diffusion gradient causing signal dropout artifacts. Common practices opt to discard gradients bearing signal attenu...
Source: Proceedings - International Symposium on Biomedical Imaging - March 4, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A Novel Framework for Groupwise Registration of fMRI Images based on Common Functional Networks.
Authors: Zhao Y, Zhang S, Chen H, Zhang W, Jinglei L, Jiang X, Shen D, Liu T Abstract Accurate registration plays a critical role in group-wise functional Magnetic Resonance Imaging (fMRI) image analysis, as spatial correspondence among different brain images is a prerequisite for inferring meaningful patterns. However, the problem is challenging and remains open, and more effort should be made to advance the state-of-the-art image registration methods for fMRI images. Inspired by the observation that common functional networks can be reconstructed from fMRI image across individuals, we propose a novel computationa...
Source: Proceedings - International Symposium on Biomedical Imaging - December 27, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Mapping age effects along fiber tracts in young adults.
We examined linear and non-linear age effects on diffusivity measures, pointwise along tracts. All diffusivity measures showed both linear and non-linear age effects. Tracts with the most pronounced age effects were those that connected the temporal lobe to the rest of the brain. Nonlinear age effects were picked up strongly in the anterior corpus callosum and right temporo-parietal tracts. PMID: 29201279 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - December 6, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A network approach to examining injury severity in pediatric tbi.
A NETWORK APPROACH TO EXAMINING INJURY SEVERITY IN PEDIATRIC TBI. Proc IEEE Int Symp Biomed Imaging. 2017;2017:105-108 Authors: Dennis EL, Rashid F, Jahanshad N, Babikian T, Mink R, Babbitt C, Johnson J, Giza CC, Asarnow RF, Thompson PM Abstract Traumatic brain injury (TBI) is the leading cause of death and disability in children, and can lead to long lasting functional impairment. Many factors influence outcome, but imaging studies examining effects of individual variables are limited by sample size. Roughly 20-40% of hospitalized TBI patients experience seizures, but not all of these patients go on t...
Source: Proceedings - International Symposium on Biomedical Imaging - December 6, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Graph theoretical approaches towards understanding differences in frontoparietal and default mode networks in Autism.
Authors: Riedel BC, Jahanshad N, Thompson PM Abstract Autism Spectrum Disorder is a complex developmental disorder affecting 1 in 68 children in the United States. While the prevalence may be on the rise, we currently lack a firm understanding of the etiology of the disease, and diagnosis is made purely on behavioral observation and informant report. As one method to improve our understanding of the disease, the current study took a systems-level approach by assessing the causal interactions among the frontoparietal and default mode networks using structural covariance of a large Autism dataset. Although preliminar...
Source: Proceedings - International Symposium on Biomedical Imaging - December 6, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

The impact of matching functional on atrophy measurement from geodesic shooting in diffeomorphisms.
THE IMPACT OF MATCHING FUNCTIONAL ON ATROPHY MEASUREMENT FROM GEODESIC SHOOTING IN DIFFEOMORPHISMS. Proc IEEE Int Symp Biomed Imaging. 2017;2017:873-877 Authors: Fleishman GM, Thompson PM Abstract Longitudinal registration has been used to map brain atrophy and tissue loss patterns over time, in both healthy and demented subjects. However, we have not seen a thorough application of the geodesic shooting in diffeomorphisms framework for this task. The registration model is complex and several choices must be made that may significantly impact the quality of results. One of these decisions is which image...
Source: Proceedings - International Symposium on Biomedical Imaging - December 6, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A comparison of network definitions for detecting sex differences in brain connectivity using support vector machines.
A COMPARISON OF NETWORK DEFINITIONS FOR DETECTING SEX DIFFERENCES IN BRAIN CONNECTIVITY USING SUPPORT VECTOR MACHINES. Proc IEEE Int Symp Biomed Imaging. 2017;2017:961-965 Authors: Hafzalla GW, Ragothaman A, Faskowitz J, Jahanshad N, McMahon KL, de Zubicaray GI, Wright MJ, Braskie MN, Prasad G, Thompson PM Abstract Human brain connectomics is a rapidly evolving area of research, using various methods to define connections or interactions between pairs of regions. Here we evaluate how the choice of (1) regions of interest, (2) definitions of a connection, and (3) normalization of connection weights to t...
Source: Proceedings - International Symposium on Biomedical Imaging - December 6, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Approximating principal genetic components of subcortical shape.
APPROXIMATING PRINCIPAL GENETIC COMPONENTS OF SUBCORTICAL SHAPE. Proc IEEE Int Symp Biomed Imaging. 2017;2017:1226-1230 Authors: Gutman BA, Pizzagalli F, Jahanshad N, Wright MJ, McMahon KL, de Zubicaray G, Thompson PM Abstract Optimal representations of the genetic structure underlying complex neuroimaging phenotypes lie at the heart of our quest to discover the genetic code of the brain. Here, we suggest a strategy for achieving such a representation by decomposing the genetic covariance matrix of complex phenotypes into maximally heritable and genetically independent components. We show that such a r...
Source: Proceedings - International Symposium on Biomedical Imaging - December 6, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Longitudinal multi-scale mapping of infant cortical folding using spherical wavelets.
This study provides valuable insights into the longitudinal changes of infant cortical folding. PMID: 29098066 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - November 4, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Mahalanobis distance for class averaging of cryo-em images.
MAHALANOBIS DISTANCE FOR CLASS AVERAGING OF CRYO-EM IMAGES. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:654-658 Authors: Bhamre T, Zhao Z, Singer A Abstract Single particle reconstruction (SPR) from cryo-electron microscopy (EM) is a technique in which the 3D structure of a molecule needs to be determined from its contrast transfer function (CTF) affected, noisy 2D projection images taken at unknown viewing directions. One of the main challenges in cryo-EM is the typically low signal to noise ratio (SNR) of the acquired images. 2D classification of images, followed by class averaging, improves the...
Source: Proceedings - International Symposium on Biomedical Imaging - October 31, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Segmentation of organs at risk in thoracic ct images using a sharpmask architecture and conditional random fields.
SEGMENTATION OF ORGANS AT RISK IN THORACIC CT IMAGES USING A SHARPMASK ARCHITECTURE AND CONDITIONAL RANDOM FIELDS. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:1003-1006 Authors: Trullo R, Petitjean C, Ruan S, Dubray B, Nie D, Shen D Abstract Cancer is one of the leading causes of death worldwide. Radiotherapy is a standard treatment for this condition and the first step of the radiotherapy process is to identify the target volumes to be targeted and the healthy organs at risk (OAR) to be protected. Unlike previous methods for automatic segmentation of OAR that typically use local information and i...
Source: Proceedings - International Symposium on Biomedical Imaging - October 25, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Generative method to discover emphysema subtypes with unsupervised learning using lung macroscopic patterns (lmps): the mesa copd study.
GENERATIVE METHOD TO DISCOVER EMPHYSEMA SUBTYPES WITH UNSUPERVISED LEARNING USING LUNG MACROSCOPIC PATTERNS (LMPS): THE MESA COPD STUDY. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:375-378 Authors: Song J, Yang J, Smith B, Balte P, Hoffman EA, Barr RG, Laine AF, Angelini ED Abstract Pulmonary emphysema overlaps considerably with chronic obstructive pulmonary disease (COPD), and is traditionally subcategorized into three subtypes: centrilobular emphysema (CLE), panlobular emphysema (PLE) and paraseptal emphysema (PSE). Automated classification methods based on supervised learning are generally base...
Source: Proceedings - International Symposium on Biomedical Imaging - October 11, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Optimal signal recovery from interleaved fmri data.
In this study, we examine the most popular methods of STC, and propose a new optimal method based on the fundamental properties of sampling theory. We evaluate this method using 20 simulated fMRI data and demonstrate the utility of STC in general as well as the superiority of the proposed method in comparison to existing ones. PMID: 28966719 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - October 4, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Empowering cortical thickness measures in clinical diagnosis of alzheimer's disease with spherical sparse coding.
EMPOWERING CORTICAL THICKNESS MEASURES IN CLINICAL DIAGNOSIS OF ALZHEIMER'S DISEASE WITH SPHERICAL SPARSE CODING. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:446-450 Authors: Zhang J, Fan Y, Li Q, Thompson PM, Ye J, Wang Y Abstract Cortical thickness estimation performed in vivo via magnetic resonance imaging (MRI) is an important technique for the diagnosis and understanding of the progression of Alzheimer's disease (AD). Directly using raw cortical thickness measures as features with Support Vector Machine (SVM) for clinical group classification only yields modest results since brain areas are n...
Source: Proceedings - International Symposium on Biomedical Imaging - October 1, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Enhancing Diffusion MRI Measures By Integrating Grey and White Matter Morphometry With Hyperbolic Wasserstein Distance.
Authors: Zhang W, Shi J, Yu J, Zhan L, Thompson PM, Wang Y Abstract In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the major features, variation of the structural connectivity and the cortical surface morphometry provide different views of structural changes to determine whether AD is present on presymptomatic patients. However, the large scale tensor-valued information and relatively low imaging resolution in diffusion MRI (dMRI) have created huge challe...
Source: Proceedings - International Symposium on Biomedical Imaging - September 24, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Ct and mri fusion for postimplant prostate brachytherapy evaluation.
CT AND MRI FUSION FOR POSTIMPLANT PROSTATE BRACHYTHERAPY EVALUATION. Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:625-628 Authors: Dehghan E, Le Y, Lee J, Song DY, Fichtinger G, Prince JL Abstract Postoperative evaluation of prostate brachytherapy is typically performed using CT, which does not have sufficient soft tissue contrast for accurate anatomy delineation. MR-CT fusion enables more accurate localization of both anatomy and implanted radioactive seeds, and hence, improves the accuracy of postoperative dosimetry. We propose a method for automatic registration of MR and CT images without a nee...
Source: Proceedings - International Symposium on Biomedical Imaging - September 14, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Architectural patterns for differential diagnosis of proliferative breast lesions from histopathological images.
ARCHITECTURAL PATTERNS FOR DIFFERENTIAL DIAGNOSIS OF PROLIFERATIVE BREAST LESIONS FROM HISTOPATHOLOGICAL IMAGES. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:152-155 Authors: Nguyen L, Tosun AB, Fine JL, Taylor DL, Chennubhotla SC Abstract The differential diagnosis of proliferative breast lesions, benign usual ductal hyperplasia (UDH) versus malignant ductal carcinoma in situ (DCIS) is challenging. This involves a pathologist examining histopathologic sections of a biopsy using a light microscope, evaluating tissue structures for their architecture or size, and assessing individual cell nuclei for...
Source: Proceedings - International Symposium on Biomedical Imaging - September 13, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Dynamic registration for gigapixel serial whole slide images.
DYNAMIC REGISTRATION FOR GIGAPIXEL SERIAL WHOLE SLIDE IMAGES. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:424-428 Authors: Rossetti BJ, Wang F, Zhang P, Teodoro G, Brat DJ, Kong J Abstract High-throughput serial histology imaging provides a new avenue for the routine study of micro-anatomical structures in a 3D space. However, the emergence of serial whole slide imaging poses a new registration challenge, as the gigapixel image size precludes the direct application of conventional registration techniques. In this paper, we develop a three-stage registration with multi-resolution mapping and propag...
Source: Proceedings - International Symposium on Biomedical Imaging - August 15, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automated level set segmentation of histopathologic cells with sparse shape prior support and dynamic occlusion constraint.
AUTOMATED LEVEL SET SEGMENTATION OF HISTOPATHOLOGIC CELLS WITH SPARSE SHAPE PRIOR SUPPORT AND DYNAMIC OCCLUSION CONSTRAINT. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:718-722 Authors: Zhang P, Wang F, Teodoro G, Liang Y, Brat D, Kong J Abstract In this paper, we propose a novel segmentation method for cells in histopathologic images based on a sparse shape prior guided variational level set framework. We automate the cell contour initialization by detecting seeds and deform contours by minimizing a new energy functional that incorporates a shape term involving sparse shape priors, an adaptive con...
Source: Proceedings - International Symposium on Biomedical Imaging - August 8, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Lars network filtration in the study of eeg brain connectivity.
LARS NETWORK FILTRATION IN THE STUDY OF EEG BRAIN CONNECTIVITY. Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:30-33 Authors: Wang Y, Chung MK, Bachhuber DRW, Schaefer SM, van Reekum CM, Davidson RJ Abstract In a brain network, weak and non-significant edge weights between nodes signal spurious connections and are often discarded by thresholding the weights. Traditional practice of thresholding edge weights at an arbitrary value can be problematic. Network filtration provides an alternative by summarizing the changes in the network topology with respect to a broad range of thresholds. A well establis...
Source: Proceedings - International Symposium on Biomedical Imaging - July 29, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Topological epilepsy seizure detection in electroencephalographic signals.
TOPOLOGICAL EPILEPSY SEIZURE DETECTION IN ELECTROENCEPHALOGRAPHIC SIGNALS. Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:351-354 Authors: Wang Y, Ombao H, Chung MK Abstract We propose a seizure detection method for electroencephalographic (EEG) epilepsy data based on a novel multi-scale topological technique called persistent homology (PH). Among several PH descriptors, persistence landscape (PL) possesses many desirable properties for rigorous statistical inference. By building PLs on EEG epilepsy signals smoothed by a weighted Fourier series (WFS) expansion, we compared the before and during phase...
Source: Proceedings - International Symposium on Biomedical Imaging - July 29, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Non-rigid registration guided by landmarks and learning.
NON-RIGID REGISTRATION GUIDED BY LANDMARKS AND LEARNING. Proc IEEE Int Symp Biomed Imaging. 2012 May;2012:704-707 Authors: Eckl J, Daum V, Hornegger J, Pohl KM Abstract Registration methods frequently rely on prior information in order to generate anatomical meaningful transformations between medical scans. In this paper, we propose a novel intensity based non-rigid registration framework, which is guided by landmarks and a regularizer based on Principle Component Analysis (PCA). Unlike existing methods in this domain, the computational complexity of our approach reduces with the number of landmarks. F...
Source: Proceedings - International Symposium on Biomedical Imaging - June 22, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Nonrigid volume registration using second-order mrf model.
NONRIGID VOLUME REGISTRATION USING SECOND-ORDER MRF MODEL. Proc IEEE Int Symp Biomed Imaging. 2012 May;2012:708-711 Authors: Kwon D, Yun ID, Pohl KM, Davatzikos C, Lee SU Abstract In this paper, we introduce a nonrigid registration method using a Markov Random Field (MRF) energy model with second-order smoothness priors. The registration determines an optimal labeling of the MRF energy model where the label corresponds to a 3D displacement vector. In the MRF energy model, spatial relationships are constructed between nodes using second-order smoothness priors. This model improves limitations of first-o...
Source: Proceedings - International Symposium on Biomedical Imaging - June 22, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Disease classification and prediction via semi-supervised dimensionality reduction.
We present a new semi-supervised algorithm for dimensionality reduction which exploits information of unlabeled data in order to improve the accuracy of image-based disease classification based on medical images. We perform dimensionality reduction by adopting the formalism of constrained matrix decomposition of [1] to semi-supervised learning. In addition, we add a new regularization term to the objective function to better captur the affinity between labeled and unlabeled data. We apply our method to a data set consisting of medical scans of subjects classified as Normal Control (CN) and Alzheimer (AD). The unlabeled dat...
Source: Proceedings - International Symposium on Biomedical Imaging - June 15, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automatic Segmentation of Breast Carcinomas from DCE-MRI using a Statistical Learning Algorithm.
Authors: Jayender J, Vosburgh KG, Gombos E, Ashraf A, Kontos D, Gavenonis SC, Jolesz FA, Pohl K Abstract Segmenting regions of high angiogenic activity corresponding to malignant tumors from DCE-MRI is a time-consuming task requiring processing of data in 4 dimensions. Quantitative analyses developed thus far are highly sensitive to external factors and are valid only under certain operating assumptions, which need not be valid for breast carcinomas. In this paper, we have developed a novel Statistical Learning Algorithm for Tumor Segmentation (SLATS) for automatically segmenting cancer from a region selected by th...
Source: Proceedings - International Symposium on Biomedical Imaging - June 15, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Segmentation of myocardium using deformable regions and graph cuts.
SEGMENTATION OF MYOCARDIUM USING DEFORMABLE REGIONS AND GRAPH CUTS. Proc IEEE Int Symp Biomed Imaging. 2012 May;2012:254-257 Authors: UzunbaĊŸ MG, Zhang S, Pohl KM, Metaxas D, Axel L Abstract Deformable models and graph cuts are two standard image segmentation techniques. Combining some of their benefits, we introduce a new segmentation system for (semi-) automatic delineation of epicardium and endocardium of Left Ventricle of the heart in Magnetic Resonance Images (MRI). Specifically, a temporal information among consecutive phases is exploited via a coupling between deformable models and graph cuts w...
Source: Proceedings - International Symposium on Biomedical Imaging - June 15, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Anatomical guided segmentation with non-stationary tissue class distributions in an expectation-maximization framework.
ANATOMICAL GUIDED SEGMENTATION WITH NON-STATIONARY TISSUE CLASS DISTRIBUTIONS IN AN EXPECTATION-MAXIMIZATION FRAMEWORK. Proc IEEE Int Symp Biomed Imaging. 2004 Apr;2004:81-84 Authors: Pohl KM, Bouix S, Kikinis R, Grimson WEL Abstract High quality segmentation of brain MR images is a challenging task. To deal with this problem many automatic segmentation methods rely on atlas information of anatomical structures. We further investigate this line of research by introducing hierarchical representations of anatomical structures in an Expectation-Maximization framework. This new approach enables us to divid...
Source: Proceedings - International Symposium on Biomedical Imaging - June 10, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Monitoring slowly evolving tumors.
MONITORING SLOWLY EVOLVING TUMORS. Proc IEEE Int Symp Biomed Imaging. 2008 May;2008:812-815 Authors: Konukoglu E, Wells WM, Novellas S, Ayache N, Kikinis R, Black PM, Pohl KM Abstract Change detection is a critical task in the diagnosis of many slowly evolving pathologies. This paper describes an approach that semi-automatically performs this task using longitudinal medical images. We are specifically interested in meningiomas, which experts often find difficult to monitor as the tumor evolution can be obscured by image artifacts. We test the method on synthetic data with known tumor growth as well as ...
Source: Proceedings - International Symposium on Biomedical Imaging - June 10, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Combining regional metrics for disease-related brain population analysis.
COMBINING REGIONAL METRICS FOR DISEASE-RELATED BRAIN POPULATION ANALYSIS. Proc IEEE Int Symp Biomed Imaging. 2012 May;2012:1515-1518 Authors: Ye DH, Hamm J, Pohl KM Abstract In this paper, we present a new metric combining regional measurements to improve image based population studies that use manifold learning techniques. These studies currently rely on a single score over the whole brain image domain. Thus, they require large amount of training data to uncover spatially complex variation in the whole brain impacted by diseases. We reduce the impact of this issue by first computing pairwise measureme...
Source: Proceedings - International Symposium on Biomedical Imaging - June 10, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Auto-encoding of discriminating morphometry from cardiac mri.
AUTO-ENCODING OF DISCRIMINATING MORPHOMETRY FROM CARDIAC MRI. Proc IEEE Int Symp Biomed Imaging. 2014 Apr-May;2014:217-221 Authors: Ye DH, Desjardins B, Ferrari V, Metaxas D, Pohl KM Abstract We propose a fully-automatic morphometric encoding targeted towards differentiating diseased from healthy cardiac MRI. Existing encodings rely on accurate segmentations of each scan. Segmentation generally includes labour-intensive editing and increases the risk associated with intra- and inter-rater variability. Our morphometric framework only requires the segmentation of a template scan. This template is non-rig...
Source: Proceedings - International Symposium on Biomedical Imaging - June 10, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Optimizing Stimulus Patterns for Dense Array tDCS With Fewer Sources Than Electrodes Using A branch and Bound Algorithm.
We present simulation results for both focal and spatially extended cortical ROIs. Our results suggest that only a few (2-3) independently controlled current sources can achieve comparable results to a full set (125 sources) to a tolerance of 5%. BB is computationally 3-5 orders of magnitude less demanding than exhaustive search. PMID: 28479959 [PubMed - in process] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - May 11, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Evaluation of Numerical Techniques for Solving the Current Injection Problem in Biological Tissues.
Authors: Hyde DE, Dannhauer M, Warfield SK, MacLeod R, Brooks DH Abstract Accurate computational modeling of electric fields in the human head has become important in clinical research to study or influence brain functionality. While existing numerical approaches have been evaluated against simple geometries with known closed form solutions, the relationship between these approaches in more complex geometries has not been studied. Here, we compare the three most commonly used approaches for bioelectric modeling: the finite element method (FEM), the finite difference method (FDM), and the boundary element method (BE...
Source: Proceedings - International Symposium on Biomedical Imaging - May 11, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automatic muscle perimysium annotation using deep convolutional neural network.
AUTOMATIC MUSCLE PERIMYSIUM ANNOTATION USING DEEP CONVOLUTIONAL NEURAL NETWORK. Proc IEEE Int Symp Biomed Imaging. 2015 Apr;2015:205-208 Authors: Sapkota M, Xing F, Su H, Yang L Abstract Diseased skeletal muscle expresses mononuclear cell infiltration in the regions of perimysium. Accurate annotation or segmentation of perimysium can help biologists and clinicians to determine individualized patient treatment and allow for reasonable prognostication. However, manual perimysium annotation is time consuming and prone to inter-observer variations. Meanwhile, the presence of ambiguous patterns in muscle im...
Source: Proceedings - International Symposium on Biomedical Imaging - April 27, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques.
AUTOMATED CELL COUNTING AND CLUSTER SEGMENTATION USING CONCAVITY DETECTION AND ELLIPSE FITTING TECHNIQUES. Proc IEEE Int Symp Biomed Imaging. 2009 Jun-Jul;2009:795-798 Authors: Kothari S, Chaudry Q, Wang MD Abstract This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmenta...
Source: Proceedings - International Symposium on Biomedical Imaging - April 13, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Development of a novel 2D color map for interactive segmentation of histological images.
We present a color segmentation approach based on a two-dimensional color map derived from the input image. Pathologists stain tissue biopsies with various colored dyes to see the expression of biomarkers. In these images, because of color variation due to inconsistencies in experimental procedures and lighting conditions, the segmentation used to analyze biological features is usually ad-hoc. Many algorithms like K-means use a single metric to segment the image into different color classes and rarely provide users with powerful color control. Our 2D color map interactive segmentation technique based on human color percept...
Source: Proceedings - International Symposium on Biomedical Imaging - April 13, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Robust Cell Segmentation for Histological Images of Glioblastoma.
Authors: Kong J, Zhang P, Liang Y, Teodoro G, Brat DJ, Wang F Abstract Glioblastoma (GBM) is a malignant brain tumor with uniformly dismal prognosis. Quantitative analysis of GBM cells is an important avenue to extract latent histologic disease signatures to correlate with molecular underpinnings and clinical outcomes. As a prerequisite, a robust and accurate cell segmentation is required. In this paper, we present an automated cell segmentation method that can satisfactorily address segmentation of overlapped cells commonly seen in GBM histology specimens. This method first detects cells with seed connectivity, di...
Source: Proceedings - International Symposium on Biomedical Imaging - April 11, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Ventricular maps in 804 subjects correlate with cognitive decline, csf pathology, and imminent alzheimer's disease.
VENTRICULAR MAPS IN 804 SUBJECTS CORRELATE WITH COGNITIVE DECLINE, CSF PATHOLOGY, AND IMMINENT ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2010 Apr;2010:241-244 Authors: Chou YY, Leporé N, Madsen SK, Saharan P, Hua X, Jack CR, Shaw LM, Trojanowski JQ, Weiner MW, Toga AW, Thompson PM, Alzheimer’s Disease Neuroimaging Initiative (ADNI) Abstract There is an urgent need for neuroimaging biomarkers of Alzheimer's disease (AD) that correlate with cognitive decline, and with accepted measures of pathology detectable in cerebrospinal fluid (CSF). Ideal biomarkers should also be able to...
Source: Proceedings - International Symposium on Biomedical Imaging - March 24, 2017 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research