Bending loss regularized network for nuclei segmentation in histopathology images.
BENDING LOSS REGULARIZED NETWORK FOR NUCLEI SEGMENTATION IN HISTOPATHOLOGY IMAGES. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:258-262 Authors: Wang H, Xian M, Vakanski A Abstract Separating overlapped nuclei is a major challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on public datasets; however, their performance in segmenting overlapped nuclei are limited. To address the issue, we propose the bending loss regularized network for nuclei segmentation. The proposed bending loss defines high penalties to contour points with large c...
Source: Proceedings - International Symposium on Biomedical Imaging - December 15, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Stan: small tumor-aware network for breast ultrasound image segmentation.
STAN: SMALL TUMOR-AWARE NETWORK FOR BREAST ULTRASOUND IMAGE SEGMENTATION. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1469-1473 Authors: Shareef B, Xian M, Vakanski A Abstract Breast tumor segmentation provides accurate tumor boundary, and serves as a key step toward further cancer quantification. Although deep learning-based approaches have been proposed and achieved promising results, existing approaches have difficulty in detecting small breast tumors. The capacity to detecting small tumors is particularly important in finding early stage cancers using computer-aided diagnosis (CAD) systems. In...
Source: Proceedings - International Symposium on Biomedical Imaging - December 15, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Brain network connectivity from matching cortical feature densities.
We present a new method for constructing structural inference brain networks from functional measures of cortical features. Instead of averaging vertex-wise cortical features, we propose the use of full functions of spatial densities of measures such as thickness and use two dimensional pairwise correlations between regions to construct population networks. We show increased within group correlations for both healthy controls and toddlers with prenatal alcohol exposure compared to the existing mean-based correlation approach. Further, we also show significant differences in brain connectivity between the healthy controls a...
Source: Proceedings - International Symposium on Biomedical Imaging - December 12, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Multi-scale Unrolled Deep Learning Framework for Accelerated Magnetic Resonance Imaging.
Authors: Nakarmi U, Cheng JY, Rios EP, Mardani M, Pauly JM, Ying L, Vasanawala SS Abstract Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-centric deep learning frameworks due to its fast inference time and 'one-parameter-fit-all' principle unlike in traditional model-based acceleration techniques. Unrolled deep learning framework that combines the deep priors and model knowledge are robust compared to naive deep learning based framework. In this paper, we propose a ...
Source: Proceedings - International Symposium on Biomedical Imaging - December 8, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Diagnostic image quality assessment and classification in medical imaging: opportunities and challenges.
DIAGNOSTIC IMAGE QUALITY ASSESSMENT AND CLASSIFICATION IN MEDICAL IMAGING: OPPORTUNITIES AND CHALLENGES. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:337-340 Authors: Ma JJ, Nakarmi U, Kin CYS, Sandino CM, Cheng JY, Syed AB, Wei P, Pauly JM, Vasanawala SS Abstract Magnetic Resonance Imaging (MRI) suffers from several artifacts, the most common of which are motion artifacts. These artifacts often yield images that are of non-diagnostic quality. To detect such artifacts, images are prospectively evaluated by experts for their diagnostic quality, which necessitates patient-revisits and rescans wheneve...
Source: Proceedings - International Symposium on Biomedical Imaging - December 5, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Improving Diagnosis of Autism Spectrum Disorder and Disentangling its Heterogeneous Functional Connectivity Patterns Using Capsule Networks.
Authors: Jiao Z, Li H, Fan Y Abstract Functional connectivity (FC) analysis is an appealing tool to aid diagnosis and elucidate the neurophysiological underpinnings of autism spectrum disorder (ASD). Many machine learning methods have been developed to distinguish ASD patients from healthy controls based on FC measures and identify abnormal FC patterns of ASD. Particularly, several studies have demonstrated that deep learning models could achieve better performance for ASD diagnosis than conventional machine learning methods. Although promising classification performance has been achieved by the existing machine le...
Source: Proceedings - International Symposium on Biomedical Imaging - December 2, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection.
Authors: Li Y, Fan Y Abstract Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art nodule detection methods, we develop a novel deep 3D convolutional neural network with an Encoder-Decoder structure in conjunction with a region proposal network. Particularly, we utilize a dynamically scaled cross entropy loss to reduce the f...
Source: Proceedings - International Symposium on Biomedical Imaging - December 2, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A generalized framework of pathlength associated community estimation for brain structural network.
In this study, we propose a novel generalized framework to solve this negative edge issue in extracting the modular structure from brain structural network. We have compared our framework with traditional Q method. The results clearly demonstrated that our framework has significant advantages in both stability and sensitivity. PMID: 33173559 [PubMed] (Source: Proceedings - International Symposium on Biomedical Imaging)
Source: Proceedings - International Symposium on Biomedical Imaging - November 12, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A list-mode osem-based attenuation and scatter compensation method for spect.
A LIST-MODE OSEM-BASED ATTENUATION AND SCATTER COMPENSATION METHOD FOR SPECT. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:646-650 Authors: Rahman MA, Laforest R, Jha AK Abstract Reliable attenuation and scatter compensation (ASC) is a pre-requisite for quantification and beneficial for visual interpretation tasks in SPECT. In this paper, we develop a reconstruction method that uses the entire SPECT emission data, i.e. data in both the photopeak and scatter windows, acquired in list-mode format and including the energy attribute of the detected photon, to perform ASC. We implemented a GPU-based ver...
Source: Proceedings - International Symposium on Biomedical Imaging - October 20, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Prediction of pivotal response treatment outcome with task fmri using random forest and variable selection.
PREDICTION OF PIVOTAL RESPONSE TREATMENT OUTCOME WITH TASK FMRI USING RANDOM FOREST AND VARIABLE SELECTION. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:97-100 Authors: Zhuang J, Dvornek NC, Li X, Yang D, Ventola P, Duncan JS Abstract Behavior intervention has shown promise for treatment for young children with autism spectrum disorder (ASD). However, current therapeutic decisions are based on trial and error, often leading to suboptimal outcomes. We propose an approach that employs task-based fMRI for early outcome prediction. Our strategy is based on the general linear model (GLM) and a random fo...
Source: Proceedings - International Symposium on Biomedical Imaging - October 7, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A semi-supervised joint learning approach to left ventricular segmentation and motion tracking in echocardiography.
A SEMI-SUPERVISED JOINT LEARNING APPROACH TO LEFT VENTRICULAR SEGMENTATION AND MOTION TRACKING IN ECHOCARDIOGRAPHY. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1734-1737 Authors: Ta K, Ahn SS, Lu A, Stendahl JC, Sinusas AJ, Duncan JS Abstract Accurate interpretation and analysis of echocardiography is important in assessing cardiovascular health. However, motion tracking often relies on accurate segmentation of the myocardium, which can be difficult to obtain due to inherent ultrasound properties. In order to address this limitation, we propose a semi-supervised joint learning network that exploit...
Source: Proceedings - International Symposium on Biomedical Imaging - October 3, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

2-channel convolutional 3d deep neural network (2cc3d) for fmri analysis: asd classification and feature learning.
2-CHANNEL CONVOLUTIONAL 3D DEEP NEURAL NETWORK (2CC3D) FOR FMRI ANALYSIS: ASD CLASSIFICATION AND FEATURE LEARNING. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1252-1255 Authors: Li X, Dvornek NC, Papademetris X, Zhuang J, Staib LH, Ventola P, Duncan JS Abstract In this paper, we propose a new whole brain fMRI-analysis scheme to identify autism spectrum disorder (ASD) and explore biological markers in ASD classification. To utilize both spatial and temporal information in fMRI, our method investigates the potential benefits of using a sliding window over time to measure temporal statistics (mean an...
Source: Proceedings - International Symposium on Biomedical Imaging - September 30, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A novel spatio-temporal hub identification method for dynamic functional networks.
A NOVEL SPATIO-TEMPORAL HUB IDENTIFICATION METHOD FOR DYNAMIC FUNCTIONAL NETWORKS. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1416-1419 Authors: Chen A, Yang D, Yan C, Peng Z, Kim M, Laurienti PJ, Wu G Abstract Functional connectivity (FC) has been widely investigated to understand the cognition and behavior that emerge from human brain. Recently, there is overwhelming evidence showing that quantifying temporal changes in FC may provide greater insight into fundamental properties of brain network. However, scant attentions has been given to characterize the functional dynamics of network organiza...
Source: Proceedings - International Symposium on Biomedical Imaging - September 17, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Characterizing the propagation pattern of neurodegeneration in alzheimer's disease by longitudinal network analysis.
CHARACTERIZING THE PROPAGATION PATTERN OF NEURODEGENERATION IN ALZHEIMER'S DISEASE BY LONGITUDINAL NETWORK ANALYSIS. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:292-295 Authors: Wang Y, Yang D, Li Q, Kaufer D, Styner M, Wu G Abstract Converging evidence shows that Alzheimer's disease (AD) is a neurodegenerative disease that represents a disconnection syndrome, whereby a large-scale brain network is progressively disrupted by one or more neuropathological processes. However, the mechanism by which pathological entities spread across a brain network is largely unknown. Since pathological burden may ...
Source: Proceedings - International Symposium on Biomedical Imaging - September 16, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Characterizing frequency-selective network vulnerability for alzheimer's disease by identifying critical harmonic patterns.
CHARACTERIZING FREQUENCY-SELECTIVE NETWORK VULNERABILITY FOR ALZHEIMER'S DISEASE BY IDENTIFYING CRITICAL HARMONIC PATTERNS. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1009-1012 Authors: Leinwand B, Wu G, Pipiras V Abstract Alzheimer's disease (AD) is a multi-factor neurodegenerative disease that selectively affects certain regions of the brain while other areas remain unaffected. The underlying mechanisms of this selectivity, however, are still largely elusive. To address this challenge, we propose a novel longitudinal network analysis method employing sparse logistic regression to identify frequ...
Source: Proceedings - International Symposium on Biomedical Imaging - September 16, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Enriching statistical inferences on brain connectivity for alzheimer's disease analysis via latent space graph embedding.
The objective is to identify regions of interests (ROIs) in the brain that are affected by topological changes of brain connectivity due to specific neurodegenerative diseases by enriching statistical group analysis. We tackle this problem by learning a latent space where statistical inference can be made more effectively. Our experiments on a large-scale Alzheimer's Disease dataset show promising result identifying ROIs that show statistically significant group differences separating even early and late Mild Cognitive Impairment (MCI) groups whose effect sizes are very subtle. PMID: 32922658 [PubMed] (Source: Proceedi...
Source: Proceedings - International Symposium on Biomedical Imaging - September 16, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Generalizable multi-site training and testing of deep neural networks using image normalization.
GENERALIZABLE MULTI-SITE TRAINING AND TESTING OF DEEP NEURAL NETWORKS USING IMAGE NORMALIZATION. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:348-351 Authors: Onofrey JA, Casetti-Dinescu DI, Lauritzen AD, Sarkar S, Venkataraman R, Fan RE, Sonn GA, Sprenkle PC, Staib LH, Papademetris X Abstract The ability of medical image analysis deep learning algorithms to generalize across multiple sites is critical for clinical adoption of these methods. Medical imging data, especially MRI, can have highly variable intensity characteristics across different individuals, scanners, and sites. However, it is not p...
Source: Proceedings - International Symposium on Biomedical Imaging - September 4, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Learning to detect brain lesions from noisy annotations.
LEARNING TO DETECT BRAIN LESIONS FROM NOISY ANNOTATIONS. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1910-1914 Authors: Karimi D, Peters JM, Ouaalam A, Prabhu SP, Sahin M, Krueger DA, Kolevzon A, Eng C, Warfield SK, Gholipour A Abstract Supervised training of deep neural networks in medical imaging applications relies heavily on expert-provided annotations. These annotations, however, are often imperfect, as voxel-by-voxel labeling of structures on 3D images is difficult and laborious. In this paper, we focus on one common type of label imperfection, namely, false negatives. Focusing on brain lesi...
Source: Proceedings - International Symposium on Biomedical Imaging - September 4, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automatic brain organ segmentation with 3d fully convolutional neural network for radiation therapy treatment planning.
AUTOMATIC BRAIN ORGAN SEGMENTATION WITH 3D FULLY CONVOLUTIONAL NEURAL NETWORK FOR RADIATION THERAPY TREATMENT PLANNING. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:758-762 Authors: Duanmu H, Kim J, Kanakaraj P, Wang A, Joshua J, Kong J, Wang F Abstract 3D organ contouring is an essential step in radiation therapy treatment planning for organ dose estimation as well as for optimizing plans to reduce organs-at-risk doses. Manual contouring is time-consuming and its inter-clinician variability adversely affects the outcomes study. Such organs also vary dramatically on sizes - up to two orders of magn...
Source: Proceedings - International Symposium on Biomedical Imaging - August 18, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Diffeomorphic smoothing for retinotopic mapping.
DIFFEOMORPHIC SMOOTHING FOR RETINOTOPIC MAPPING. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:534-538 Authors: Tu Y, Ta D, Lu ZL, Wang Y Abstract Retinotopic mapping, the mapping of visual input on the retina to cortical neurons, is an important topic in vision science. Typically, cortical neurons are related to visual input on the retina using functional magnetic resonance imaging (fMRI) of cortical responses to slowly moving visual stimuli on the retina. Although it is well known from neurophysiology studies that retinotopic mapping is locally diffeomorphic (i.e., smooth, differentiable, and inve...
Source: Proceedings - International Symposium on Biomedical Imaging - August 13, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A convolutional autoencoder approach to learn volumetric shape representations for brain structures.
A CONVOLUTIONAL AUTOENCODER APPROACH TO LEARN VOLUMETRIC SHAPE REPRESENTATIONS FOR BRAIN STRUCTURES. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1559-1562 Authors: Yu EM, Sabuncu MR Abstract We propose a novel machine learning strategy for studying neuroanatomical shape variation. Our model works with volumetric binary segmentation images, and requires no preprocessing such as the extraction of surface points or a mesh. The learned shape descriptor is invariant to affine transformations, including shifts, rotations and scaling. Thanks to the adopted autoencoder framework, inter-subject differences...
Source: Proceedings - International Symposium on Biomedical Imaging - August 13, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A new registration approach for dynamic analysis of calcium signals in organs.
A NEW REGISTRATION APPROACH FOR DYNAMIC ANALYSIS OF CALCIUM SIGNALS IN ORGANS. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:934-937 Authors: Liang P, Chen J, Brodskiy PA, Wu Q, Zhang Y, Zhang Y, Yang L, Zartman JJ, Chen DZ Abstract Wing disc pouches of fruit flies are a powerful genetic model for studying physiological intercellular calcium (Ca 2+) signals for dynamic analysis of cell signaling in organ development and disease studies. A key to analyzing spatial-temporal patterns of Ca 2+ signal waves is to accurately align the pouches across image sequences. However, pouches in different image fra...
Source: Proceedings - International Symposium on Biomedical Imaging - July 28, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Liver steatosis segmentation with deep learning methods.
LIVER STEATOSIS SEGMENTATION WITH DEEP LEARNING METHODS. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:24-27 Authors: Guo X, Wang F, Teodoro G, Farris AB, Kong J Abstract Liver steatosis is known as the abnormal accumulation of lipids within cells. An accurate quantification of steatosis area within the liver histopathological microscopy images plays an important role in liver disease diagnosis and transplantation assessment. Such a quantification analysis often requires a precise steatosis segmentation that is challenging due to abundant presence of highly overlapped steatosis droplets. In this pap...
Source: Proceedings - International Symposium on Biomedical Imaging - July 18, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Spatially informed cnn for automated cone detection in adaptive optics retinal images.
SPATIALLY INFORMED CNN FOR AUTOMATED CONE DETECTION IN ADAPTIVE OPTICS RETINAL IMAGES. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:1383-1386 Authors: Jin H, Morgan JIW, Gee JC, Chen M Abstract Adaptive optics (AO) scanning laser ophthalmoscopy offers cellular level in-vivo imaging of the human cone mosaic. Existing analysis of cone photoreceptor density in AO images require accurate identification of cone cells, which is a time and labor-intensive task. Recently, several methods have been introduced for automated cone detection in AO retinal images using convolutional neural networks (CNN). Howeve...
Source: Proceedings - International Symposium on Biomedical Imaging - July 11, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Brain age estimation using lstm on children's brain mri.
BRAIN AGE ESTIMATION USING LSTM ON CHILDREN'S BRAIN MRI. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:420-423 Authors: He S, Gollub RL, Murphy SN, Perez JD, Prabhu S, Pienaar R, Robertson RL, Grant PE, Ou Y Abstract Brain age prediction based on children's brain MRI is an important biomarker for brain health and brain development analysis. In this paper, we consider the 3D brain MRI volume as a sequence of 2D images and propose a new framework using the recurrent neural network for brain age estimation. The proposed method is named as 2D-ResNet18+Long short-term memory (LSTM), which consists of fou...
Source: Proceedings - International Symposium on Biomedical Imaging - July 8, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Compensatory brain connection discovery in alzheimer's disease.
COMPENSATORY BRAIN CONNECTION DISCOVERY IN ALZHEIMER'S DISEASE. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:283-287 Authors: Aganj I, Frau-Pascual A, Iglesias JE, Yendiki A, Augustinack JC, Salat DH, Fischl B Abstract Identification of the specific brain networks that are vulnerable or resilient in neurodegenerative diseases can help to better understand the disease effects and derive new connectomic imaging biomarkers. In this work, we use brain connectivity to find pairs of structural connections that are negatively correlated with each other across Alzheimer's disease (AD) and healthy populatio...
Source: Proceedings - International Symposium on Biomedical Imaging - June 28, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Automatic labeling of cortical sulci using spherical convolutional neural networks in a developmental cohort.
AUTOMATIC LABELING OF CORTICAL SULCI USING SPHERICAL CONVOLUTIONAL NEURAL NETWORKS IN A DEVELOPMENTAL COHORT. Proc IEEE Int Symp Biomed Imaging. 2020 Apr;2020:412-415 Authors: Hao L, Bao S, Tang Y, Gao R, Parvathaneni P, Miller JA, Voorhies W, Yao J, Bunge SA, Weiner KS, Landman BA, Lyu I Abstract In this paper, we present the automatic labeling framework for sulci in the human lateral prefrontal cortex (PFC). We adapt an existing spherical U-Net architecture with our recent surface data augmentation technique to improve the sulcal labeling accuracy in a developmental cohort. Specifically, our framewor...
Source: Proceedings - International Symposium on Biomedical Imaging - June 18, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Discovering Salient Anatomical Landmarks by Predicting Human Gaze.
Authors: Droste R, Chatelain P, Drukker L, Sharma H, Papageorghiou AT, Noble JA Abstract Anatomical landmarks are a crucial prerequisite for many medical imaging tasks. Usually, the set of landmarks for a given task is predefined by experts. The landmark locations for a given image are then annotated manually or via machine learning methods trained on manual annotations. In this paper, in contrast, we present a method to automatically discover and localize anatomical landmarks in medical images. Specifically, we consider landmarks that attract the visual attention of humans, which we term visually salient landmarks...
Source: Proceedings - International Symposium on Biomedical Imaging - June 5, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Self-Supervised Representation Learning for Ultrasound Video.
Authors: Jiao J, Droste R, Drukker L, Papageorghiou AT, Noble JA Abstract Recent advances in deep learning have achieved promising performance for medical image analysis, while in most cases ground-truth annotations from human experts are necessary to train the deep model. In practice, such annotations are expensive to collect and can be scarce for medical imaging applications. Therefore, there is significant interest in learning representations from unlabelled raw data. In this paper, we propose a self-supervised learning approach to learn meaningful and transferable representations from medical imaging video with...
Source: Proceedings - International Symposium on Biomedical Imaging - June 5, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Emphysema classification using a multi-view convolutional network.
EMPHYSEMA CLASSIFICATION USING A MULTI-VIEW CONVOLUTIONAL NETWORK. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:519-522 Authors: Bermejo-Peláez D, San José Estépar R, Ledesma-Carbayo MJ Abstract In this article we propose and validate a fully automatic tool for emphysema classification in Computed Tomography (CT) images. We hypothesize that a relatively simple Convolutional Neural Network (CNN) architecture can learn even better discriminative features from the input data compared with more complex and deeper architectures. The proposed architecture is comprised of only 4 convo...
Source: Proceedings - International Symposium on Biomedical Imaging - May 29, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

A sr-net 3d-to-2d architecture for paraseptal emphysema segmentation.
A SR-NET 3D-TO-2D ARCHITECTURE FOR PARASEPTAL EMPHYSEMA SEGMENTATION. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:303-306 Authors: Bermejo-Peláez D, Okajima Y, Washko GR, Ledesma-Carbayo MJ, San José Estépar R Abstract Paraseptal emphysema (PSE) is a relatively unexplored emphysema subtype that is usually asymptomatic, but recently associated with interstitial lung abnormalities which are related with clinical outcomes, including mortality. Previous local-based methods for emphysema subtype quantification do not properly characterize PSE. This is in part for their inability to...
Source: Proceedings - International Symposium on Biomedical Imaging - May 29, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Localizing image-based biomarker regression without training masks: a new approach to biomarker discovery.
We present a methodology to regress the biomarker from the image while simultaneously computing the quality control image. Our proposed methodology does not require segmentation masks for training, but infers the segmentations directly from the pixels that used to compute the biomarker value. The network proposed consists of two steps: a segmentation method to an unknown reference and a summation method for the biomarker estimation. The network is optimized using a dual loss function, L2 for the biomarkers and an L1 to enforce sparsity. We showcase our methodology in the problem of pectoralis muscle area (PMA) and subcutan...
Source: Proceedings - International Symposium on Biomedical Imaging - May 28, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Regression of the navier-stokes equation solutions for pulmonary airway flow using neural networks.
REGRESSION OF THE NAVIER-STOKES EQUATION SOLUTIONS FOR PULMONARY AIRWAY FLOW USING NEURAL NETWORKS. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1229-1233 Authors: de Los Ojos Araúzo D, Nardelli P, San José Estépar R Abstract Computerized fluid dynamics models of particle deposition in the human airways are used to characterize deposition patterns that enable the study of lung diseases like asthma and chronic obstructive pulmonary disease (COPD). Despite this fact, the influence of patient-specific geometry on the deposition efficiency and patterns is not well documented nor mo...
Source: Proceedings - International Symposium on Biomedical Imaging - May 28, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Prediction of treatment outcome for autism from structure of the brain based on sure independence screening.
PREDICTION OF TREATMENT OUTCOME FOR AUTISM FROM STRUCTURE OF THE BRAIN BASED ON SURE INDEPENDENCE SCREENING. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:404-408 Authors: Zhuang J, Dvornek NC, Zhao Q, Li X, Ventola P, Duncan JS Abstract Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and behavioral treatment interventions have shown promise for young children with ASD. However, there is limited progress in understanding the effect of each type of treatment. In this project, we aim to detect structural changes in the brain after treatment and select structural features assoc...
Source: Proceedings - International Symposium on Biomedical Imaging - April 9, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Learning 3d white matter microstructure from 2d histology.
In this study, we use diffusion MRI (dMRI) of a squirrel monkey brain and corresponding myelin stained sections in combination with a convolution neural network to learn the relationship between the 3D diffusion estimated axonal fiber orientation distributions and the 2D myelin stain. We find that we are able to estimate the 3D fiber distribution with moderate to high angular agreement with the ground truth (median angular correlation coefficients of 0.48 across the unseen slices). This network could be used to validate dMRI neuronal structural measurements in 3D, even if only 2D histology is available for validation. Gene...
Source: Proceedings - International Symposium on Biomedical Imaging - March 27, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Voting-based segmentation of overlapping nuclei in clarity images.
We present a cell nucleus segmentation method that is formulated as a parameter estimation problem with the goal of determining the count, shapes, and locations of nuclei that most accurately describe an image. We applied our new voting-based approach to fluorescence confocal microscopy images of neural tissue stained with DAPI, which highlights nuclei. Compared to manual counting of cells in three DAPI images, our method outperformed three existing approaches. On a manually labeled high-resolution DAPI image, our method also outperformed those methods and achieved a cell count accuracy of 98.99% and mean Dice coefficient ...
Source: Proceedings - International Symposium on Biomedical Imaging - February 12, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Spatio-Temporal Partitioning and Description of Full-Length Routine Fetal Anomaly Ultrasound Scans.
Authors: Sharma H, Droste R, Chatelain P, Drukker L, Papageorghiou AT, Noble JA Abstract This paper considers automatic clinical workflow description of full-length routine fetal anomaly ultrasound scans using deep learning approaches for spatio-temporal video analysis. Multiple architectures consisting of 2D and 2D + t CNN, LSTM, and convolutional LSTM are investigated and compared. The contributions of short-term and long-term temporal changes are studied, and a multi-stream framework analysis is found to achieve the best top-1 accuracy=0.77 and top-3 accuracy=0.94. Automated partitioning and characterisation on ...
Source: Proceedings - International Symposium on Biomedical Imaging - January 30, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Cortical foldingprints for infant identification.
This study thus aims to address two important questions in neuroscience: 1) whether the infant cortical folding is unique for individual identification; and 2) considering the region-specific inter-subject variability, which cortical regions are more distinct and reliable for infant identification. To this end, we propose a novel discriminative descriptor of regional cortical folding based on multi-scale analysis of curvature maps via spherical wavelets, called FoldingPrint. Experiments are carried out on a large longitudinal dataset with 1,141 MRI scans from 472 infants. Despite the dramatic development in the first two y...
Source: Proceedings - International Symposium on Biomedical Imaging - January 16, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

Acceleration controlled diffeomorphisms for nonparametric image regression.
ACCELERATION CONTROLLED DIFFEOMORPHISMS FOR NONPARAMETRIC IMAGE REGRESSION. Proc IEEE Int Symp Biomed Imaging. 2019 Apr;2019:1488-1491 Authors: Fishbaugh J, Gerig G Abstract The analysis of medical image time-series is becoming increasingly important as longitudinal imaging studies are maturing and large scale open imaging databases are becoming available. Image regression is widely used for several purposes: as a statistical representation for hypothesis testing, to bring clinical scores and images not acquired at the same time into temporal correspondence, or as a consistency filter to enforce tempor...
Source: Proceedings - International Symposium on Biomedical Imaging - January 16, 2020 Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research

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