Deep convolutional neural networks for imaging data based survival analysis of rectal cancer.
DEEP CONVOLUTIONAL NEURAL NETWORKS FOR IMAGING DATA BASED SURVIVAL ANALYSIS OF RECTAL CANCER. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:846-849 Authors: Li H, Boimel P, Janopaul-Naylor J, Zhong H, Xiao Y, Ben-Josef E, Fan Y Abstract Recent radiomic studies have witnessed promising performance of deep learning techniques in learning radiomic features and fusing multimodal imaging data. Most existing deep learning based radiomic studies build predictive models in a setting of pattern classification, not appropriate for survival analysis studies where some data samples have incomplete observations....
Source: Proceedings - International Symposium on Biomedical Imaging - January 15, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Accelerated coronary mri using 3d spirit-raki with sparsity regularization.
In this study, we sought to extend this approach to arbitrary sampling patterns, using coil self-consistency. This technique, called SPIRiT-RAKI, utilizes scan-specific convolutional neural networks to nonlinearly enforce coil self-consistency. Additionally, regularization terms can also be incorporated. SPIRiT-RAKI was used to accelerate right coronary MRI. Reconstructions were compared to SPIRiT for different undersampling patterns and acceleration rates. Results show SPIRiT-RAKI reduces residual aliasing and blurring artifacts compared to SPIRiT. PMID: 31893013 [PubMed] (Source: Proceedings - International Symposium...
Source: Proceedings - International Symposium on Biomedical Imaging - January 3, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Multi-layer Fast Level Set Segmentation for Macular OCT.
Authors: Liu Y, Carass A, Solomon SD, Saidha S, Calabresi PA, Prince JL Abstract Segmenting optical coherence tomography (OCT) images of the retina is important in the diagnosis, staging, and tracking of ophthalmological diseases. Whereas automatic segmentation methods are typically much faster than manual segmentation, they may still take several minutes to segment a three-dimensional macular scan, and this can be prohibitive for routine clinical application. In this paper, we propose a fast, multi-layer macular OCT segmentation method based on a fast level set method. In our framework, the boundary evolution oper...
Source: Proceedings - International Symposium on Biomedical Imaging - December 21, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Diagnosis status guided brain imaging genetics via integrated regression and sparse canonical correlation analysis.
DIAGNOSIS STATUS GUIDED BRAIN IMAGING GENETICS VIA INTEGRATED REGRESSION AND SPARSE CANONICAL CORRELATION ANALYSIS. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:356-359 Authors: Du L, Liu K, Yao X, Risacher SL, Guo L, Saykin AJ, Shen L, ADNI Abstract Brain imaging genetics use the imaging quantitative traits (QTs) as intermediate endophenotypes to identify the genetic basis of the brain structure, function and abnormality. The regression and canonical correlation analysis (CCA) coupled with sparsity regularization are widely used in imaging genetics. The regression only selects relevant features fo...
Source: Proceedings - International Symposium on Biomedical Imaging - December 19, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Early prediction of alzheimer's disease dementia based on baseline hippocampal mri and 1-year follow-up cognitive measures using deep recurrent neural networks.
EARLY PREDICTION OF ALZHEIMER'S DISEASE DEMENTIA BASED ON BASELINE HIPPOCAMPAL MRI AND 1-YEAR FOLLOW-UP COGNITIVE MEASURES USING DEEP RECURRENT NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:368-371 Authors: Li H, Fan Y, Alzheimer’s Disease Neuroimaging Initiative Abstract Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to...
Source: Proceedings - International Symposium on Biomedical Imaging - December 7, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Collaborative clustering of subjects and radiomic features for predicting clinical outcomes of rectal cancer patients.
COLLABORATIVE CLUSTERING OF SUBJECTS AND RADIOMIC FEATURES FOR PREDICTING CLINICAL OUTCOMES OF RECTAL CANCER PATIENTS. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1303-1306 Authors: Liu H, Li H, Boimel P, Janopaul-Naylor J, Zhong H, Xiao Y, Ben-Josef E, Fan Y Abstract Most machine learning approaches in radiomics studies ignore the underlying difference of radiomic features computed from heterogeneous groups of patients, and intrinsic correlations of the features are not fully exploited yet. In order to better predict clinical outcomes of cancer patients, we adopt an unsupervised machine learning ...
Source: Proceedings - International Symposium on Biomedical Imaging - December 7, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Fully-automatic segmentation of kidneys in clinical ultrasound images using a boundary distance regression network.
In this study, we developed a novel boundary distance regression deep neural network to segment the kidneys, informed by the fact that the kidney boundaries are relatively consistent across images in terms of their appearance. Particularly, we first use deep neural networks pre-trained for classification of natural images to extract high-level image features from ultrasound images, then these feature maps are used as input to learn kidney boundary distance maps using a boundary distance regression network, and finally the predicted boundary distance maps are classified as kidney pixels or non-kidney pixels using a pixel cl...
Source: Proceedings - International Symposium on Biomedical Imaging - December 7, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Accurate segmentation of brain images into 34 structures combining a non-stationary adaptive statistical atlas and a multi-atlas with applications to Alzheimer's disease.
Authors: Yan Z, Zhang S, Liu X, Metaxas DN, Montillo A, AIBL Abstract Accurate segmentation of the 30+ subcortical structures in MR images of whole diseased brains is challenging due to inter-subject variability and complex geometry of brain anatomy. However a clinically viable solution yielding precise segmentation of the structures would enable: 1) accurate, objective measurement of structure volumes many of which are associated with diseases such as Alzheimer's, 2) therapy monitoring and 3) drug development. Our contributions are two-fold. First we construct an extended adaptive statistical atlas method (EASA) t...
Source: Proceedings - International Symposium on Biomedical Imaging - December 6, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

3d fully convolutional networks for co-segmentation of tumors on pet-ct images.
3D FULLY CONVOLUTIONAL NETWORKS FOR CO-SEGMENTATION OF TUMORS ON PET-CT IMAGES. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:228-231 Authors: Zhong Z, Kim Y, Zhou L, Plichta K, Allen B, Buatti J, Wu X Abstract Positron emission tomography and computed tomography (PET-CT) dual-modality imaging provides critical diagnostic information in modern cancer diagnosis and therapy. Automated accurate tumor delineation is essentially important in computer-assisted tumor reading and interpretation based on PET-CT. In this paper, we propose a novel approach for the segmentation of lung tumors that combines the ...
Source: Proceedings - International Symposium on Biomedical Imaging - November 30, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Multi-scale segmentation using deep graph cuts: robust lung tumor delineation in mvcbct.
MULTI-SCALE SEGMENTATION USING DEEP GRAPH CUTS: ROBUST LUNG TUMOR DELINEATION IN MVCBCT. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:514-518 Authors: Wu X, Zhong Z, Buatti J, Bai J Abstract Deep networks have been used in a growing trend in medical image analysis with the remarkable progress in deep learning. In this paper, we formulate the multi-scale segmentation as a Markov Random Field (MRF) energy minimization problem in a deep network (graph), which can be efficiently and exactly solved by computing a minimum s-t cut in an appropriately constructed graph. The performance of the proposed meth...
Source: Proceedings - International Symposium on Biomedical Imaging - November 30, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Improving tumor co-segmentation on pet-ct images with 3d co-matting.
IMPROVING TUMOR CO-SEGMENTATION ON PET-CT IMAGES WITH 3D CO-MATTING. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:224-227 Authors: Zhong Z, Kim Y, Zhou L, Plichta K, Allen B, Buatti J, Wu X Abstract Positron emission tomography and computed tomography (PET-CT) plays a critically important role in modern cancer therapy. In this paper, we focus on automated tumor delineation on PET-CT image pairs. Inspired by co-segmentation model, we develop a novel 3D image co-matting technique making use of the inner-modality information of PET and CT for matting. The obtained co-matting results are then incorpora...
Source: Proceedings - International Symposium on Biomedical Imaging - November 27, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Changes in resting state MRI networks from a single season of football distinguishes controls, low, and high head impact exposure.
Authors: Murugesan G, Famili A, Davenport E, Wagner B, Urban J, Kelley M, Jones D, Whitlow C, Stitzel J, Maldjian J, Montillo A Abstract Sub-concussive asymptomatic head impacts during contact sports may develop potential neurological changes and may have accumulative effect through repetitive occurrences in contact sports like American football. The effects of sub-concussive head impacts on the functional connectivity of the brain are still unclear with no conclusive results yet presented. Although various studies have been performed on the topic, they have yielded mixed results with some concluding that sub concu...
Source: Proceedings - International Symposium on Biomedical Imaging - November 21, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automatic Identification of Successful Memory Encoding in Stereo EEG of Refractory, Mesial Temporal Lobe Epilepsy.
Authors: Famili A, Krishnan G, Davenport E, Germi J, Wagner B, Lega B, Montillo A Abstract Surgical resection of portions of the temporal lobe is the standard of care for patients with refractory mesial temporal lobe epilepsy. While this reduces seizures, it often results in an inability to form new memories, which leads to difficulties in social situations, learning, and suboptimal quality of life. Learning about the success or failure to form new memory in such patients is critical if we are to generate neuromodulation-based therapies. To this end, we tackle the many challenges in analyzing memory formation when ...
Source: Proceedings - International Symposium on Biomedical Imaging - November 21, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Sensitivity of derived clinical biomarkers to rs-fMRI preprocessing software versions.
Authors: Nguyen KP, Fatt CC, Mellema C, Trivedi MH, Montillo A Abstract When common software packages (CONN and SPM) are used to process fMRI, results such as functional connectivity measures can substantially differ depending on the versions of the packages used and the tools used to convert image formats such as DICOM to NIFTI. The significance of these differences are illustrated within the context of a realistic research application: finding moderators of antidepressant response from a large psychiatric study of 288 major depressive disorder (MDD) patients. Significant differences in functional connectivity mea...
Source: Proceedings - International Symposium on Biomedical Imaging - November 20, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Multiple Deep Learning Architectures Achieve Superior Performance Diagnosing Autism Spectrum Disorder Using Features Previously Extracted from Structural and Functional MRI.
Authors: Mellema C, Treacher A, Nguyen K, Montillo A Abstract The diagnosis of Autism Spectrum Disorder (ASD) is a subjective process requiring clinical expertise in neurodevelopmental disorders. Since such expertise is not available at many clinics, automated diagnosis using machine learning (ML) algorithms would be of great value to both clinicians and the imaging community to increase the diagnoses' availability and reproducibility while reducing subjectivity. This research systematically compares the performance of classifiers using over 900 subjects from the IMPAC database [1], using the database's derived ana...
Source: Proceedings - International Symposium on Biomedical Imaging - November 20, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Accelerating dynamic magnetic resonance imaging by nonlinear sparse coding.
ACCELERATING DYNAMIC MAGNETIC RESONANCE IMAGING BY NONLINEAR SPARSE CODING. Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:510-513 Authors: Nakarmi U, Zhou Y, Lyu J, Slavakis K, Ying L Abstract Although being high-dimensional, dynamic magnetic resonance images usually lie on low-dimensional manifolds. Nonlinear models have been shown to capture well that latent low-dimensional nature of data, and can thus lead to improvements in the quality of constrained recovery algorithms. This paper advocates a novel reconstruction algorithm for dynamic magnetic resonance imaging (dMRI) based on nonlinear diction...
Source: Proceedings - International Symposium on Biomedical Imaging - November 14, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Accelerating magnetic resonance imaging via deep learning.
ACCELERATING MAGNETIC RESONANCE IMAGING VIA DEEP LEARNING. Proc IEEE Int Symp Biomed Imaging. 2016 Apr;2016:514-517 Authors: Wang S, Su Z, Ying L, Peng X, Zhu S, Liang F, Feng D, Liang D Abstract This paper proposes a deep learning approach for accelerating magnetic resonance imaging (MRI) using a large number of existing high quality MR images as the training datasets. An off-line convolutional neural network is designed and trained to identify the mapping relationship between the MR images obtained from zero-filled and fully-sampled k-space data. The network is not only capable of restoring fine stru...
Source: Proceedings - International Symposium on Biomedical Imaging - November 14, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Statistical inference on the number of cycles in brain networks.
In this study, we present a new scalable algorithm for enumerating the number of cycles in the network. We show that the number of cycles is monotonically decreasing with respect to the filtration values during graph filtration. We further develop a new statistical inference framework for determining the significance of the number of cycles. The methods are applied in determining if the number of cycles is a statistically significant heritable network feature in the functional human brain network. PMID: 31687091 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - November 9, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Sparse infomax based on hoyer projection and its application to simulated structural mri and snp data.
SPARSE INFOMAX BASED ON HOYER PROJECTION AND ITS APPLICATION TO SIMULATED STRUCTURAL MRI AND SNP DATA. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:418-421 Authors: Duan K, Silva RF, Chen J, Lin D, Calhoun VD, Liu J Abstract Independent component analysis has been widely applied to brain imaging and genetic data analyses for its ability to identify interpretable latent sources. Nevertheless, leveraging source sparsity in a more granular way may further improve its ability to optimize the solution for certain data types. For this purpose, we propose a sparse infomax algorithm based on nonlinear Hoye...
Source: Proceedings - International Symposium on Biomedical Imaging - November 9, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Frnet: flattened residual network for infant mri skull stripping.
FRNET: FLATTENED RESIDUAL NETWORK FOR INFANT MRI SKULL STRIPPING. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:999-1002 Authors: Zhang Q, Wang L, Zong X, Lin W, Li G, Shen D Abstract Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image witho...
Source: Proceedings - International Symposium on Biomedical Imaging - November 6, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A preliminary volumetric mri study of amygdala and hippocampal subfields in autism during infancy.
A PRELIMINARY VOLUMETRIC MRI STUDY OF AMYGDALA AND HIPPOCAMPAL SUBFIELDS IN AUTISM DURING INFANCY. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1052-1056 Authors: Li G, Chen MH, Li G, Wu D, Sun Q, Shen D, Wang L Abstract Currently, autism spectrum disorder (ASD) is mainly diagnosed by the observation of core behavioral symptoms. Consequently, the window of opportunity for effective intervention may have passed, when the disorder is detected until 3 years of age. Thus, it is of great importance to identify imaging-based biomarkers for early diagnosis of ASD. Previous findings indicate that an abnorm...
Source: Proceedings - International Symposium on Biomedical Imaging - November 6, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Spherical u-net for infant cortical surface parcellation.
SPHERICAL U-NET FOR INFANT CORTICAL SURFACE PARCELLATION. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1882-1886 Authors: Zhao F, Xia S, Wu Z, Wang L, Chen Z, Lin W, Gilmore JH, Shen D, Li G Abstract In human brain MRI studies, it is of great importance to accurately parcellate cortical surfaces into anatomically and functionally meaningful regions. In this paper, we propose a novel end-to-end deep learning method by formulating surface parcellation as a semantic segmentation task on the sphere. To extend the convolutional neural networks (CNNs) to the spherical space, corresponding operations of s...
Source: Proceedings - International Symposium on Biomedical Imaging - November 6, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Deep learning-based assessment of tumor-associated stroma for diagnosing breast cancer in histopathology images.
DEEP LEARNING-BASED ASSESSMENT OF TUMOR-ASSOCIATED STROMA FOR DIAGNOSING BREAST CANCER IN HISTOPATHOLOGY IMAGES. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:929-932 Authors: Bejnordi BE, Lin J, Glass B, Mullooly M, Gierach GL, Sherman ME, Karssemeijer N, van der Laak J, Beck AH Abstract Diagnosis of breast carcinomas has so far been limited to the morphological interpretation of epithelial cells and the assessment of epithelial tissue architecture. Consequently, most of the automated systems have focused on characterizing the epithelial regions of the breast to detect cancer. In this paper, we pro...
Source: Proceedings - International Symposium on Biomedical Imaging - October 24, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Charting development-based joint parcellation maps of human and macaque brains during infancy.
CHARTING DEVELOPMENT-BASED JOINT PARCELLATION MAPS OF HUMAN AND MACAQUE BRAINS DURING INFANCY. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:422-425 Authors: Xia J, Wang F, Wu Z, Wang L, Wang Y, Zhang C, Lin W, Shen D, Li G Abstract Comparative characterization of early brain development between human and macaque using neuroimaging data is crucial to understand the mechanisms of brain development and evolution. To this end, joint cortical parcellation maps of human and macaque infant brains with corresponding regions are highly desirable, since they provide basic cortical parcels for both region-bas...
Source: Proceedings - International Symposium on Biomedical Imaging - July 30, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Construction of 4d neonatal cortical surface atlases using wasserstein distance.
CONSTRUCTION OF 4D NEONATAL CORTICAL SURFACE ATLASES USING WASSERSTEIN DISTANCE. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:995-998 Authors: Chen Z, Wu Z, Sun L, Wang F, Wang L, Lin W, Gilmore JH, Shen D, Li G Abstract Spatiotemporal (4D) neonatal cortical surface atlases with densely sampled ages are important tools for understanding the dynamic early brain development. Conventionally, after non-linear co-registration, surface atlases were constructed by simple Euclidean average of cortical attributes across different subjects, which leads to blurred folding patterns and therefore hampers the re...
Source: Proceedings - International Symposium on Biomedical Imaging - July 30, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Expected Label Value Computation for Atlas-Based Image Segmentation.
Authors: Aganj I, Fischl B Abstract The use of multiple atlases is common in medical image segmentation. This typically requires deformable registration of the atlases (or the average atlas) to the new image, which is computationally expensive and susceptible to entrapment in local optima. We propose to instead consider the probability of all possible transformations and compute the expected label value (ELV), thereby not relying merely on the transformation resulting from the registration. Moreover, we do so without actually performing deformable registration, thus avoiding the associated computational costs. We e...
Source: Proceedings - International Symposium on Biomedical Imaging - July 26, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Graph convolutional neural networks for alzheimer's disease classification.
GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR ALZHEIMER'S DISEASE CLASSIFICATION. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:414-417 Authors: Song TA, Roy Chowdhury S, Yang F, Jacobs H, El Fakhri G, Li Q, Johnson K, Dutta J Abstract Graph convolutional neural networks (GCNNs) aim to extend the data representation and classification capabilities of convolutional neural networks, which are highly effective for signals defined on regular Euclidean domains, e.g. image and audio signals, to irregular, graph-structured data defined on non-Euclidean domains. Graph-theoretic tools that enable us to study the b...
Source: Proceedings - International Symposium on Biomedical Imaging - July 23, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Incorporating temporal dependency on erp based bci.
INCORPORATING TEMPORAL DEPENDENCY ON ERP BASED BCI. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:752-756 Authors: Koçanaoğulları A, Quivira F, Erdoğmuş D Abstract In brain computer interface (BCI) systems based on event related potentials (ERPs), a windowed electroencephalography (EEG) signal is taken into consideration for the assumed duration of the ERP potential. In BCI applications inter stimuli interval is shorter than the ERP duration. This causes temporal dependencies over observation potentials thus disallows taking the data into consideration independently. However, conventional...
Source: Proceedings - International Symposium on Biomedical Imaging - May 23, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Improved classification in tactile bcis using a noisy label model.
In this study, we model the tactile ERP generation as label noise and develop a novel BCI paradigm for binary communication designed to minimize label confusion. The classification model is based on a modified Gaussian mixture and trained using expectation maximization (EM). Finally, we show after testing on multiple subjects that this approach yields cross-validated accuracies for all users which are significantly above chance and suggests that such an approach is robust and reliable for a variety of binary communication-based applications. PMID: 31110601 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - May 23, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

The fourier radial error spectrum plot: a more nuanced quantitative evaluation of image reconstruction quality.
THE FOURIER RADIAL ERROR SPECTRUM PLOT: A MORE NUANCED QUANTITATIVE EVALUATION OF IMAGE RECONSTRUCTION QUALITY. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:61-64 Authors: Kim TH, Haldar JP Abstract In the modern biomedical image reconstruction literature, the quality of a reconstructed image is often numerically quantified using scalar error measures such as mean-squared error or the structural similarity index. While such measures provide a rough summary of image quality, they also suffer from well-known limitations. For example, a substantial amount of information is necessarily lost whenever th...
Source: Proceedings - International Symposium on Biomedical Imaging - April 23, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

MLS: Joint Manifold-Learning and Sparsity-Aware Framework for Highly Accelerated Dynamic Magnetic Resonance Imaging.
Authors: Nakarmi U, Slavakis K, Ying L Abstract Manifold-based models have been recently exploited for accelerating dynamic magnetic resonance imaging (dMRI). While manifold-based models have shown to be more efficient than conventional low-rank approaches, joint low-rank and sparsity-aware modeling still appears to be very efficient due to the inherent sparsity within dMR images. In this paper, we propose a joint manifold-learning and sparsity-aware framework for dMRI. The proposed method establishes a link between the recently developed manifold models and conventional sparsity-aware models. Dynamic MR images are...
Source: Proceedings - International Symposium on Biomedical Imaging - April 23, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Robust autocalibrated loraks for epi ghost correction.
We present a new structured low-rank matrix recovery ghost correction method that we call Robust Autocalibrated LORAKS (RAC-LORAKS). RAC-LORAKS incorporates constraints from autocalibration data to avoid ill-posedness, but allows adaptation of these constraints to gain robustness against possible autocalibration imperfections. RAC-LORAKS is tested in two challenging scenarios: highly-undersampled multi-channel EPI of the brain, and cardiac EPI with a double-oblique slice orientation. Results show that RAC-LORAKS can provide substantial improvements over existing ghost correction methods, and potentially enables new imaging...
Source: Proceedings - International Symposium on Biomedical Imaging - April 17, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

SonoEyeNet: Standardized Fetal Ultrasound Plane Detection Informed by Eye Tracking.
We present a novel automated approach for detection of standardized abdominal circumference (AC) planes in fetal ultrasound built in a convolutional neural network (CNN) framework, called SonoEyeNet, that utilizes eye movement data of a sonographer in automatic interpretation. Eye movement data was collected from experienced sonographers as they identified an AC plane in fetal ultrasound video clips. A visual heatmap was generated from the eye movements for each video frame. A CNN model was built using ultrasound frames and their corresponding visual heatmaps. Different methods of processing visual heatmaps and their fusio...
Source: Proceedings - International Symposium on Biomedical Imaging - April 13, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Towards optimal linear estimation of orientation distribution functions with arbitrarily sampled diffusion mri data.
TOWARDS OPTIMAL LINEAR ESTIMATION OF ORIENTATION DISTRIBUTION FUNCTIONS WITH ARBITRARILY SAMPLED DIFFUSION MRI DATA. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:743-746 Authors: Varadarajan D, Haldar JP Abstract The estimation of orientation distribution functions (ODFs) from diffusion MRI data is an important step in diffusion tractography, but existing estimation methods often depend on signal modeling assumptions that are violated by real data, lack theoretical characterization, and/or are only applicable to a small range of q-space sampling patterns. As a result, existing ODF estimation method...
Source: Proceedings - International Symposium on Biomedical Imaging - April 10, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

M-MRI: A Manifold-based Framework to Highly Accelerated Dynamic Magnetic Resonance Imaging.
Authors: Nakarmi U, Slavakis K, Lyu J, Ying L Abstract High-dimensional signals, including dynamic magnetic resonance (dMR) images, often lie on low dimensional manifold. While many current dynamic magnetic resonance imaging (dMRI) reconstruction methods rely on priors which promote low-rank and sparsity, this paper proposes a novel manifold-based framework, we term M-MRI, for dMRI reconstruction from highly undersampled k-space data. Images in dMRI are modeled as points on or close to a smooth manifold, and the underlying manifold geometry is learned through training data, called "navigator" signals. Mor...
Source: Proceedings - International Symposium on Biomedical Imaging - April 10, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automatic body localization and brain ventricle segmentation in 3d high frequency ultrasound images of mouse embryos.
AUTOMATIC BODY LOCALIZATION AND BRAIN VENTRICLE SEGMENTATION IN 3D HIGH FREQUENCY ULTRASOUND IMAGES OF MOUSE EMBRYOS. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:635-639 Authors: Kuo JW, Qiu Z, Aristizabal O, Mamou J, Turnbull DH, Ketterling J, Wang Y Abstract This paper presents a fully automatic segmentation system for whole-body high-frequency ultrasound (HFU) images of mouse embryos that can simultaneously segment the body contour and the brain ventricles (BVs). Our system first locates a region of interest (ROI), which covers the interior of the uterus, by sub-surface analysis. Then, it segme...
Source: Proceedings - International Symposium on Biomedical Imaging - March 26, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Active learning guided interactions for consistent image segmentation with reduced user interactions.
We present extensive experimental evaluation of our results on two different publicly available datasets. PMID: 30881602 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - March 21, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Probing in vivo microstructure with t 1-t 2 relaxation correlation spectroscopic imaging.
PROBING IN VIVO MICROSTRUCTURE WITH T 1-T 2 RELAXATION CORRELATION SPECTROSCOPIC IMAGING. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:675-678 Authors: Kim D, Wisnowski JL, Nguyen CT, Haldar JP Abstract Quantitative MR relaxometry can provide unique subvoxel information about the microscopic tissue compartments that are present in a large imaging voxel. However, unambiguously distinguishing between these tissue compartments continues to be challenging with conventional methods due to the illposedness of the inverse problem. This paper describes a new imaging approach, which we call T 1 Relaxation-T...
Source: Proceedings - International Symposium on Biomedical Imaging - March 13, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Registration of brain resection mri with intensity and location priors.
REGISTRATION OF BRAIN RESECTION MRI WITH INTENSITY AND LOCATION PRIORS. Proc IEEE Int Symp Biomed Imaging. 2011 Mar-Apr;2011:1520-1523 Authors: Chitphakdithai N, Vives KP, Duncan JS Abstract Images with missing correspondences are difficult to align using standard registration methods due to the assumption that the same features appear in both images. To address this problem in brain resection images, we have recently proposed an algorithm in which the registration process is aided by an indicator map that is simultaneously estimated to distinguish between missing and valid tissue. We now extend our me...
Source: Proceedings - International Symposium on Biomedical Imaging - February 21, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Data-driven cluster selection for subcortical shape and cortical thickness predicts recovery from depressive symptoms.
DATA-DRIVEN CLUSTER SELECTION FOR SUBCORTICAL SHAPE AND CORTICAL THICKNESS PREDICTS RECOVERY FROM DEPRESSIVE SYMPTOMS. Proc IEEE Int Symp Biomed Imaging. 2017 Apr;2017:502-506 Authors: Wade BSC, Sui J, Njau S, Leaver AM, Vasvada M, Gutman BA, Thompson PM, Espinoza R, Woods RP, Abbott CC, Narr KL, Joshi SH Abstract Patients with major depressive disorder (MDD) who do not achieve full symptomatic recovery after antidepressant treatment have a higher risk of relapse. Compared to pharmacotherapies, electroconvulsive therapy (ECT) more rapidly produces a greater extent of response in severely depressed pati...
Source: Proceedings - International Symposium on Biomedical Imaging - February 7, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Global pdf-based temporal non-local means filtering reveals individual differences in brain connectivity.
GLOBAL PDF-BASED TEMPORAL NON-LOCAL MEANS FILTERING REVEALS INDIVIDUAL DIFFERENCES IN BRAIN CONNECTIVITY. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:15-19 Authors: Li J, Choi S, Joshi AA, Wisnowski JL, Leahy RM Abstract Characterizing functional brain connectivity using resting fMRI is challenging due to the relatively small BOLD signal contrast and low SNR. Gaussian filtering tends to undermine the individual differences detected by analysis of BOLD signal by smoothing signals across boundaries of different functional areas. Temporal non-local means (tNLM) filtering denoises fMRI data while pres...
Source: Proceedings - International Symposium on Biomedical Imaging - February 5, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Detection and tracking of migrating oligodendrocyte progenitor cells from in vivo fluorescence time-lapse imaging data.
DETECTION AND TRACKING OF MIGRATING OLIGODENDROCYTE PROGENITOR CELLS FROM IN VIVO FLUORESCENCE TIME-LAPSE IMAGING DATA. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:961-964 Authors: Wang Y, Ali M, Wang Y, Kucenas S, Yu G Abstract In this work, we develop a fully automatic algorithm named "MCDT" (Migrating Cell Detector and Tracker) for the integrated task of migrating cell detection, segmentation and tracking from in vivo fluorescence time-lapse microscopy imaging data. The interest of detecting and tracking migrating cells arouses from the scientific question in understanding the impact ...
Source: Proceedings - International Symposium on Biomedical Imaging - January 2, 2019 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A new registration method based on log-euclidean tensor metrics and its application to genetic studies.
A NEW REGISTRATION METHOD BASED ON LOG-EUCLIDEAN TENSOR METRICS AND ITS APPLICATION TO GENETIC STUDIES. Proc IEEE Int Symp Biomed Imaging. 2008 May;2008:1115-1118 Authors: Brun C, Leporé N, Pennec X, Chou YY, Lee AD, de Zubicaray G, McMahon K, Wright M, Barysheva M, Toga AW, Thompson PM Abstract In structural brain MRI, group differences or changes in brain structures can be detected using Tensor-Based Morphometry (TBM). This method consists of two steps: (1) a non-linear registration step, that aligns all of the images to a common template, and (2) a subsequent statistical analysis. The numerou...
Source: Proceedings - International Symposium on Biomedical Imaging - December 20, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A lagrangian formulation for statistical fluid registration.
A LAGRANGIAN FORMULATION FOR STATISTICAL FLUID REGISTRATION. Proc IEEE Int Symp Biomed Imaging. 2009 Jun-Jul;2009:975-978 Authors: Brun CC, Lepore N, Pennec X, Chou YY, Lee AD, Barysheva M, de Zubicaray GI, McMahon KL, Wright MJ, Toga AW, Thompson PM Abstract We defined a new statistical fluid registration method with Lagrangian mechanics. Although several authors have suggested that empirical statistics on brain variation should be incorporated into the registration problem, few algorithms have included this information and instead use regularizers that guarantee diffeomorphic mappings. Here we combin...
Source: Proceedings - International Symposium on Biomedical Imaging - December 20, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Statistically assisted fluid image registration algorithm - safira.
STATISTICALLY ASSISTED FLUID IMAGE REGISTRATION ALGORITHM - SAFIRA. Proc IEEE Int Symp Biomed Imaging. 2010 Apr;2010:364-367 Authors: Brun CC, Lepore N, Pennec X, Chou YY, Lee AD, Barysheva M, de Zubicaray GI, McMahon KL, Wright MJ, Thompson PM Abstract In this paper, we develop and validate a new Statistically Assisted Fluid Registration Algorithm (SAFIRA) for brain images. A non-statistical version of this algorithm was first implemented in [2] and re-formulated using Lagrangian mechanics in [3]. Here we extend this algorithm to 3D: given 3D brain images from a population, vector fields and their cor...
Source: Proceedings - International Symposium on Biomedical Imaging - December 20, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Multivariate variance-components analysis in dti.
MULTIVARIATE VARIANCE-COMPONENTS ANALYSIS IN DTI. Proc IEEE Int Symp Biomed Imaging. 2010 Apr;2010:1157-1160 Authors: Lee AD, Leporé N, de Leeuw J, Brun CC, Barysheva M, McMahon KL, de Zubicaray GI, Martin NG, Wright MJ, Thompson PM Abstract Twin studies are a major research direction in imaging genetics, a new field, which combines algorithms from quantitative genetics and neuroimaging to assess genetic effects on the brain. In twin imaging studies, it is common to estimate the intraclass correlation (ICC), which measures the resemblance between twin pairs for a given phenotype. In this paper, ...
Source: Proceedings - International Symposium on Biomedical Imaging - December 20, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Time resolved displacement-based registration of in vivo cdti cardiomyocyte orientations.
TIME RESOLVED DISPLACEMENT-BASED REGISTRATION OF IN VIVO CDTI CARDIOMYOCYTE ORIENTATIONS. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:474-478 Authors: Verzhbinsky IA, Magrath P, Aliotta E, Ennis DB, Perotti LE Abstract In vivo cardiac microstructure acquired using cardiac diffusion tensor imaging (cDTI) is a critical component of patient-specific models of cardiac electrophysiology and mechanics. In order to limit bulk motion artifacts and acquisition time, cDTI microstructural data is acquired at a single cardiac phase necessitating registration to the reference configuration on which the patient...
Source: Proceedings - International Symposium on Biomedical Imaging - December 20, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A Label-fusion-aided Convolutional Neural Network for Isointense Infant Brain Tissue Segmentation.
Authors: Li T, Zhou F, Zhu Z, Shu H, Zhu H Abstract The extremely low tissue contrast in white matter during an infant's isointense stage (6-8 months) of brain development presents major difficulty when segmenting brain image regions for analysis. We sought to develop a label-fusion-aided deep-learning approach for automatically segmenting isointense infant brain images into white matter, gray matter and cerebrospinal fluid using T1- and T2-weighted magnetic resonance images. A key idea of our approach is to apply the fully convolutional neural network (FCNN) to individual brain regions determined by a traditional ...
Source: Proceedings - International Symposium on Biomedical Imaging - December 19, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Best individual template selection from deformation tensor minimization.
BEST INDIVIDUAL TEMPLATE SELECTION FROM DEFORMATION TENSOR MINIMIZATION. Proc IEEE Int Symp Biomed Imaging. 2008 May;2008:460-463 Authors: Leporé N, Brun C, Chou YY, Lee AD, Barysheva M, Pennec X, McMahon KL, Meredith M, de Zubicaray GI, Wright MJ, Toga AW, Thompson PM Abstract We study the influence of the choice of template in tensor-based morphometry. Using 3D brain MR images from 10 monozygotic twin pairs, we defined a tensor-based distance in the log-Euclidean framework [1] between each image pair in the study. Relative to this metric, twin pairs were found to be closer to each other on ave...
Source: Proceedings - International Symposium on Biomedical Imaging - December 16, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Comparison of fractional and geodesic anisotropy in diffusion tensor images of 90 monozygotic and dizygotic twins.
COMPARISON OF FRACTIONAL AND GEODESIC ANISOTROPY IN DIFFUSION TENSOR IMAGES OF 90 MONOZYGOTIC AND DIZYGOTIC TWINS. Proc IEEE Int Symp Biomed Imaging. 2008 May;2008:943-946 Authors: Lee AD, Leporé N, Barysheva M, Chou YY, Brun C, Madsen SK, McMahon KL, de Zubicaray GI, Meredith M, Wright MJ, Toga AW, Thompson PM Abstract We used diffusion tensor magnetic resonance imaging (DTI) to reveal the extent of genetic effects on brain fiber microstructure, based on tensor-derived measures, in 22 pairs of monozygotic (MZ) twins and 23 pairs of dizygotic (DZ) twins (90 scans). After Log-Euclidean denoising ...
Source: Proceedings - International Symposium on Biomedical Imaging - December 16, 2018 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research