1,499 results match your criteria Medical image analysis[Journal]


Evaluating reinforcement learning agents for anatomical landmark detection.

Med Image Anal 2019 Feb 14;53:156-164. Epub 2019 Feb 14.

Biomedical Image Analysis Group (BioMedIA), Imperial College London, London, UK.

Automatic detection of anatomical landmarks is an important step for a wide range of applications in medical image analysis. Manual annotation of landmarks is a tedious task and prone to observer errors. In this paper, we evaluate novel deep reinforcement learning (RL) strategies to train agents that can precisely and robustly localize target landmarks in medical scans. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.007DOI Listing
February 2019

Multiple-correlation similarity for block-matching based fast CT to ultrasound registration in liver interventions.

Med Image Anal 2019 Feb 7;53:132-141. Epub 2019 Feb 7.

Biomedical Imaging Group Rotterdam, Departments of Radiology & Nuclear Medicine and Medical Informatics, Erasmus MC - University Medical Center Rotterdam, The Netherlands. Electronic address:

In this work we present a fast approach to perform registration of computed tomography to ultrasound volumes for image guided intervention applications. The method is based on a combination of block-matching and outlier rejection. The block-matching uses a correlation based multimodal similarity metric, where the intensity and the gradient of the computed tomography images along with the ultrasound volumes are the input images to find correspondences between blocks in the computed tomography and the ultrasound volumes. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.003DOI Listing
February 2019
1 Read

Iterative fully convolutional neural networks for automatic vertebra segmentation and identification.

Med Image Anal 2019 Feb 12;53:142-155. Epub 2019 Feb 12.

Image Sciences Institute, University Medical Center Utrecht, Room Q.02.4.45, 3508 GA Utrecht, P.O. Box 85500, The Netherlands.

Precise segmentation and anatomical identification of the vertebrae provides the basis for automatic analysis of the spine, such as detection of vertebral compression fractures or other abnormalities. Most dedicated spine CT and MR scans as well as scans of the chest, abdomen or neck cover only part of the spine. Segmentation and identification should therefore not rely on the visibility of certain vertebrae or a certain number of vertebrae. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.005DOI Listing
February 2019
1 Read

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy.

Med Image Anal 2019 Feb 2;53:123-131. Epub 2019 Feb 2.

School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.

In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids for real-time tissue characterization and can help to perform visual investigations aimed for example to discover epithelial cancers. Due to physical constraints on the acquisition process, endomicroscopy images, still today have a low number of informative pixels which hampers their quality. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.011DOI Listing
February 2019

Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data.

Med Image Anal 2019 Jan 30;53:111-122. Epub 2019 Jan 30.

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA. Electronic address:

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a particular time point. However, the underlying association among different time points in disease progression is still under-explored in previous studies. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.007DOI Listing
January 2019

Automatic needle detection and real-time Bi-planar needle visualization during 3D ultrasound scanning of the liver.

Med Image Anal 2019 Feb 2;53:104-110. Epub 2019 Feb 2.

Department of Medical Informatics, Erasmus MC, University Medical Center Rotterdam, the Netherlands; Department of Radiology & Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, the Netherlands.

2D ultrasound (US) image guidance is used in minimally invasive procedures in the liver to visualize the target and the needle. Needle insertion using 2D ultrasound keeping the transducer position to view needle and reach target is challenging. Dedicated needle holders attached to the US transducer help to target in plane and at a specific angle. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.002DOI Listing
February 2019

Novel and facile criterion to assess the accuracy of WSS estimation by 4D flow MRI.

Med Image Anal 2019 Jan 31;53:95-103. Epub 2019 Jan 31.

Department of Mechanical Engineering, Hanyang University, Seoul, 04763, South Korea; Institute of Nano Science and Technology, Hanyang University, Seoul, 04763, South Korea. Electronic address:

Four-dimensional flow magnetic resonance imaging (4D flow MRI) is a versatile tool to obtain hemodynamic information and anatomic information simultaneously. The wall shear stress (WSS), a force exerted on a vessel wall in parallel, is one of the hemodynamic parameters available with 4D flow MRI and is thought to play an important role in clinical applications such as assessing the development of atherosclerosis. Nevertheless, the accuracy of WSS obtained with 4D flow MRI is rarely evaluated or reported in literature, especially in the in vivo studies. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.009DOI Listing
January 2019
1 Read

Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space.

Med Image Anal 2019 Jan 21;53:79-94. Epub 2019 Jan 21.

Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, USA. Electronic address:

Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). Read More

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http://dx.doi.org/10.1016/j.media.2019.01.006DOI Listing
January 2019

Recurrent inference machines for reconstructing heterogeneous MRI data.

Med Image Anal 2019 Jan 18;53:64-78. Epub 2019 Jan 18.

Informatics Institute at the University of Amsterdam, Amsterdam 1098 XH, the Netherlands; AMLab, Amsterdam, 1098 XH, the Netherlands.

Deep learning allows for accelerated magnetic resonance image (MRI) reconstruction, thereby shortening measurement times. Rather than using sparsifying transforms, a prerequisite in Compressed Sensing (CS), suitable MRI prior distributions are learned from data. In clinical practice, both the underlying anatomy as well as image acquisition settings vary. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.005DOI Listing
January 2019
3 Reads

Generalised coherent point drift for group-wise multi-dimensional analysis of diffusion brain MRI data.

Med Image Anal 2019 Jan 17;53:47-63. Epub 2019 Jan 17.

iMBE Institute of Medical and Biological Engineering, University of Leeds, Leeds, UK; School of Mechanical Engineering, University of Leeds, Leeds, UK; CISTIB Centre for Computational Imaging & Simulation Technologies in Biomedicine, University of Leeds, Leeds, UK. Electronic address:

A probabilistic framework for registering generalised point sets comprising multiple voxel-wise data features such as positions, orientations and scalar-valued quantities, is proposed. It is employed for the analysis of magnetic resonance diffusion tensor image (DTI)-derived quantities, such as fractional anisotropy (FA) and fibre orientation, across multiple subjects. A hybrid Student's t-Watson-Gaussian mixture model-based non-rigid registration framework is formulated for the joint registration and clustering of voxel-wise DTI-derived data, acquired from multiple subjects. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415193000
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http://dx.doi.org/10.1016/j.media.2019.01.001DOI Listing
January 2019
4 Reads

Training recurrent neural networks robust to incomplete data: Application to Alzheimer's disease progression modeling.

Med Image Anal 2019 Jan 12;53:39-46. Epub 2019 Jan 12.

Biomediq A/S, Copenhagen, Denmark; Cerebriu A/S, Copenhagen, Denmark; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark.

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.004DOI Listing
January 2019
6 Reads

Learning to detect chest radiographs containing pulmonary lesions using visual attention networks.

Med Image Anal 2019 Jan 9;53:26-38. Epub 2019 Jan 9.

Department of Biomedical Engineering, King's College London, London, UK. Electronic address:

Machine learning approaches hold great potential for the automated detection of lung nodules on chest radiographs, but training algorithms requires very large amounts of manually annotated radiographs, which are difficult to obtain. The increasing availability of PACS (Picture Archiving and Communication System), is laying the technological foundations needed to make available large volumes of clinical data and images from hospital archives. Binary labels indicating whether a radiograph contains a pulmonary lesion can be extracted at scale, using natural language processing algorithms. Read More

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http://dx.doi.org/10.1016/j.media.2018.12.007DOI Listing
January 2019
2 Reads

Nonrigid reconstruction of 3D breast surfaces with a low-cost RGBD camera for surgical planning and aesthetic evaluation.

Med Image Anal 2019 Jan 11;53:11-25. Epub 2019 Jan 11.

Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK. Electronic address:

Accounting for 26% of all new cancer cases worldwide, breast cancer remains the most common form of cancer in women. Although early breast cancer has a favourable long-term prognosis, roughly a third of patients suffer from a suboptimal aesthetic outcome despite breast conserving cancer treatment. Clinical-quality 3D modelling of the breast surface therefore assumes an increasingly important role in advancing treatment planning, prediction and evaluation of breast cosmesis. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.003DOI Listing
January 2019
1 Read

Repetitive motion compensation for real time intraoperative video processing.

Med Image Anal 2019 Jan 4;53:1-10. Epub 2019 Jan 4.

Univ. Lyon, INSA Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, F69100, France.

In this paper, we present a motion compensation algorithm dedicated to video processing during neurosurgery. After craniotomy, the brain surface undergoes a repetitive motion due to the cardiac pulsation. This motion as well as potential video camera motion prevent accurate video analysis. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183087
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http://dx.doi.org/10.1016/j.media.2018.12.005DOI Listing
January 2019
4 Reads

Robust motion correction for cardiac T1 and ECV mapping using a T1 relaxation model approach.

Med Image Anal 2019 Feb 18;52:212-227. Epub 2018 Dec 18.

Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium; Medical Imaging Research Center, UZ Leuven, Herestraat 49 - 7003, Leuven, 3000, Belgium.

T1 and ECV mapping are quantitative methods for myocardial tissue characterization using cardiac MRI, and are highly relevant for the diagnosis of diffuse myocardial diseases. Since the maps are calculated pixel-by-pixel from a set of MRI images with different T1-weighting, it is critical to assure exact spatial correspondence between these images. However, in practice, different sources of motion e. Read More

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http://dx.doi.org/10.1016/j.media.2018.12.004DOI Listing
February 2019

MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.

Med Image Anal 2019 Feb 20;52:199-211. Epub 2018 Dec 20.

Department of Computer Science, University of Warwick, UK; Department of Pathology, University Hospitals Coventry and Warwickshire, Coventry, UK; The Alan Turing Institute, London, UK.

The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity among pathologists. The rise of computational pathology has led to the development of automated methods for gland segmentation that aim to overcome the challenges of manual segmentation. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183060
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http://dx.doi.org/10.1016/j.media.2018.12.001DOI Listing
February 2019
3 Reads

Automated diagnosis of breast ultrasonography images using deep neural networks.

Med Image Anal 2019 Feb 20;52:185-198. Epub 2018 Dec 20.

Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, PR China. Electronic address:

Ultrasonography images of breast mass aid in the detection and diagnosis of breast cancer. Manually analyzing ultrasonography images is time-consuming, exhausting and subjective. Automated analyzing such images is desired. Read More

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http://dx.doi.org/10.1016/j.media.2018.12.006DOI Listing
February 2019
3.654 Impact Factor

Towards cross-modal organ translation and segmentation: A cycle- and shape-consistent generative adversarial network.

Med Image Anal 2019 Feb 19;52:174-184. Epub 2018 Dec 19.

J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, 32611, USA. Electronic address:

Synthesized medical images have several important applications. For instance, they can be used as an intermedium in cross-modality image registration or used as augmented training samples to boost the generalization capability of a classifier. In this work, we propose a generic cross-modality synthesis approach with the following targets: 1) synthesizing realistic looking 2D/3D images without needing paired training data, 2) ensuring consistent anatomical structures, which could be changed by geometric distortion in cross-modality synthesis and 3) more importantly, improving volume segmentation by using synthetic data for modalities with limited training samples. Read More

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http://dx.doi.org/10.1016/j.media.2018.12.002DOI Listing
February 2019

Micro-Net: A unified model for segmentation of various objects in microscopy images.

Med Image Anal 2019 Feb 15;52:160-173. Epub 2018 Dec 15.

Department of Computer Science, University of Warwick, UK; Department of Pathology, University Hospitals Coventry and Warwickshire, UK; The Alan Turing Institute, London, UK. Electronic address:

Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. Read More

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http://dx.doi.org/10.1016/j.media.2018.12.003DOI Listing
February 2019

A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images.

Med Image Anal 2019 Feb 26;52:144-159. Epub 2018 Nov 26.

Biomedical Image Technologies, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain. Electronic address:

Lung vessel segmentation has been widely explored by the biomedical image processing community; however, the differentiation of arterial from venous irrigation is still a challenge. Pulmonary artery-vein (AV) segmentation using computed tomography (CT) is growing in importance owing to its undeniable utility in multiple cardiopulmonary pathological states, especially those implying vascular remodelling, allowing the study of both flow systems separately. We present a new framework to approach the separation of tree-like structures using local information and a specifically designed graph-cut methodology that ensures connectivity as well as the spatial and directional consistency of the derived subtrees. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.011DOI Listing
February 2019

A deep learning framework for unsupervised affine and deformable image registration.

Med Image Anal 2019 Feb 8;52:128-143. Epub 2018 Dec 8.

Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, The Netherlands.

Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for registration was supervised using predefined example registrations. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.010DOI Listing
February 2019

The effect of motion correction interpolation on quantitative T1 mapping with MRI.

Med Image Anal 2019 Feb 1;52:119-127. Epub 2018 Dec 1.

The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Israel. Electronic address:

Quantitative magnetic resonance imaging (qMRI) is a technique for mapping the physical properties of the underlying tissue using several MR images with different contrasts. To overcome subject motion between the acquired images, it is necessary to register the images to a common reference frame. A drawback of registration is the use of interpolation and resampling techniques, which can introduce artifacts into the interpolated data. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.012DOI Listing
February 2019

Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative.

Med Image Anal 2019 Feb 17;52:109-118. Epub 2018 Nov 17.

Therapy Planning Group, Zuse Institute Berlin, Berlin, Germany; 1000shapes GmbH, Berlin, Germany. Electronic address:

We present a method for the automated segmentation of knee bones and cartilage from magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape with Convolutional Neural Networks (CNNs). The proposed approach incorporates 3D Statistical Shape Models (SSMs) as well as 2D and 3D CNNs to achieve a robust and accurate segmentation of even highly pathological knee structures. The shape models and neural networks employed are trained using data from the Osteoarthritis Initiative (OAI) and the MICCAI grand challenge "Segmentation of Knee Images 2010" (SKI10), respectively. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.009DOI Listing
February 2019

GAS: A genetic atlas selection strategy in multi-atlas segmentation framework.

Med Image Anal 2019 Feb 19;52:97-108. Epub 2018 Nov 19.

Dep. of Medical Physics and Biomedical Engineering, University College London, U.K.; School of Biomedical Engineering and Imaging Science, Kings College London, U.K.

Multi-Atlas based Segmentation (MAS) algorithms have been successfully applied to many medical image segmentation tasks, but their success relies on a large number of atlases and good image registration performance. Choosing well-registered atlases for label fusion is vital for an accurate segmentation. This choice becomes even more crucial when the segmentation involves organs characterized by a high anatomical and pathological variability. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183086
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http://dx.doi.org/10.1016/j.media.2018.11.007DOI Listing
February 2019
2 Reads

Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification.

Med Image Anal 2019 Feb 13;52:80-96. Epub 2018 Nov 13.

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea. Electronic address:

Recent works have shown that hyper-networks derived from blood-oxygen-level-dependent (BOLD) fMRI, where an edge (called hyper-edge) can be connected to more than two nodes, are effective biomarkers for MCI classification. Although BOLD fMRI is a high temporal resolution fMRI approach to assess alterations in brain networks, it cannot pinpoint to a single correlation of neuronal activity since BOLD signals are composite. In contrast, arterial spin labeling (ASL) is a lower temporal resolution fMRI technique for measuring cerebral blood flow (CBF) that can provide quantitative, direct brain network physiology measurements. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183022
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http://dx.doi.org/10.1016/j.media.2018.11.006DOI Listing
February 2019
16 Reads
3.654 Impact Factor

Motion artifact recognition and quantification in coronary CT angiography using convolutional neural networks.

Med Image Anal 2019 Feb 15;52:68-79. Epub 2018 Nov 15.

Philips Research, Hamburg, Germany.

Excellent image quality is a primary prerequisite for diagnostic non-invasive coronary CT angiography. Artifacts due to cardiac motion may interfere with detection and diagnosis of coronary artery disease and render subsequent treatment decisions more difficult. We propose deep-learning-based measures for coronary motion artifact recognition and quantification in order to assess the diagnostic reliability and image quality of coronary CT angiography images. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.003DOI Listing
February 2019
8 Reads

Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks.

Med Image Anal 2019 Feb 26;52:56-67. Epub 2018 Oct 26.

Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.

Diffusion-weighted magnetic resonance imaging can be used to non-invasively probe the brain microstructure. In addition, recent advances have enabled the identification of complex fiber configurations present in most of the white matter. This has improved the investigation of structural connectivity with tractography methods. Read More

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http://dx.doi.org/10.1016/j.media.2018.10.009DOI Listing
February 2019

Supervised learning for bone shape and cortical thickness estimation from CT images for finite element analysis.

Med Image Anal 2019 Feb 16;52:42-55. Epub 2018 Nov 16.

Institute of Surgical Technology and Biomechanics, University of Bern, Switzerland. Electronic address:

Knowledge about the thickness of the cortical bone is of high interest for fracture risk assessment. Most finite element model solutions overlook this information because of the coarse resolution of the CT images. To circumvent this limitation, a three-steps approach is proposed. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.001DOI Listing
February 2019
1 Read
3.654 Impact Factor

CATARACTS: Challenge on automatic tool annotation for cataRACT surgery.

Med Image Anal 2019 Feb 16;52:24-41. Epub 2018 Nov 16.

Inserm, UMR 1101, Brest, F-29200, France. Electronic address:

Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.008DOI Listing
February 2019
14 Reads
3.654 Impact Factor

Quality-based UnwRap of SUbdivided Large Arrays (URSULA) for high-resolution MRI data.

Med Image Anal 2019 Feb 13;52:13-23. Epub 2018 Nov 13.

Institute of Neuroscience and Medicine - 4, Forschungszentrum Jülich GmbH, Jülich 52425, Germany; Department of Neurology, Faculty of Medicine, RWTH Aachen University, Aachen 52074, Germany; JARA - BRAIN - Translational Medicine, RWTH Aachen University, Aachen 52074, Germany; Institute of Neuroscience and Medicine - 11, Forschungszentrum Jülich GmbH, Jülich 52425, Germany; Monash Biomedical Imaging, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia. Electronic address:

In Magnetic Resonance Imaging, mapping of the static magnetic field and the magnetic susceptibility is based on multidimensional phase measurements. Phase data are ambiguous and have to be unwrapped to their true range in order to exhibit a correct representation of underlying features. High-resolution imaging at ultra-high fields, where susceptibility and phase contrast are natural tools, can generate large datasets, which tend to dramatically increase computing time demands for spatial unwrapping algorithms. Read More

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http://dx.doi.org/10.1016/j.media.2018.11.004DOI Listing
February 2019
8 Reads

Automatic graph-based method for localization of cochlear implant electrode arrays in clinical CT with sub-voxel accuracy.

Med Image Anal 2019 Feb 13;52:1-12. Epub 2018 Nov 13.

Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA. Electronic address:

Cochlear implants (CIs) are neural prosthetics that provide a sense of sound to people who experience severe to profound hearing loss. Recent studies have demonstrated a correlation between hearing outcomes and intra-cochlear locations of CI electrodes. Our group has been conducting investigations on this correlation and has been developing an image-guided cochlear implant programming (IGCIP) system to program CI devices to improve hearing outcomes. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183013
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http://dx.doi.org/10.1016/j.media.2018.11.005DOI Listing
February 2019
12 Reads

Disease quantification on PET/CT images without explicit object delineation.

Med Image Anal 2019 Jan 10;51:169-183. Epub 2018 Nov 10.

Medical Image Processing group, Department of Radiology, 3710 Hamilton Walk, Goddard Building, 6th Floor, Philadelphia, PA 19104, United States; Abramson Cancer Center, Perelman Center for Advanced Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States.

Purpose: The derivation of quantitative information from images in a clinically practical way continues to face a major hurdle because of image segmentation challenges. This paper presents a novel approach, called automatic anatomy recognition-disease quantification (AAR-DQ), for disease quantification (DQ) on positron emission tomography/computed tomography (PET/CT) images. This approach explores how to decouple DQ methods from explicit dependence on object (e. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183055
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http://dx.doi.org/10.1016/j.media.2018.11.002DOI Listing
January 2019
10 Reads

Automatic brain labeling via multi-atlas guided fully convolutional networks.

Med Image Anal 2019 Jan 1;51:157-168. Epub 2018 Nov 1.

Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, Korea. Electronic address:

Multi-atlas-based methods are commonly used for MR brain image labeling, which alleviates the burdening and time-consuming task of manual labeling in neuroimaging analysis studies. Traditionally, multi-atlas-based methods first register multiple atlases to the target image, and then propagate the labels from the labeled atlases to the unlabeled target image. However, the registration step involves non-rigid alignment, which is often time-consuming and might lack high accuracy. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183086
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http://dx.doi.org/10.1016/j.media.2018.10.012DOI Listing
January 2019
16 Reads

Recovery of 3D rib motion from dynamic chest radiography and CT data using local contrast normalization and articular motion model.

Med Image Anal 2019 Jan 19;51:144-156. Epub 2018 Oct 19.

Graduate School of Information Science, Nara Institute of Science and Technology, Japan. Electronic address:

Dynamic chest radiography (2D x-ray video) is a low-dose and cost-effective functional imaging method with high temporal resolution. While the analysis of rib-cage motion has been shown to be effective for evaluating respiratory function, it has been limited to 2D. We aim at 3D rib-motion analysis for high temporal resolution while keeping the radiation dose at a level comparable to conventional examination. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183085
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http://dx.doi.org/10.1016/j.media.2018.10.002DOI Listing
January 2019
11 Reads

Hierarchical segmentation using equivalence test (HiSET): Application to DCE image sequences.

Med Image Anal 2019 Jan 28;51:125-143. Epub 2018 Oct 28.

Université Paris Descartes, Université Sorbonne Paris Cité (USPC), France; Faculté de Pharmacie de Paris - EA bioSTM, France. Electronic address:

Dynamical contrast enhanced (DCE) imaging allows non invasive access to tissue micro-vascularization. It appears as a promising tool to build imaging biomarkers for diagnostic, prognosis or anti-angiogenesis treatment monitoring of cancer. However, quantitative analysis of DCE image sequences suffers from low signal to noise ratio (SNR). Read More

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http://dx.doi.org/10.1016/j.media.2018.10.007DOI Listing
January 2019
2 Reads
3.654 Impact Factor

A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions.

Med Image Anal 2019 Jan 2;51:116-124. Epub 2018 Nov 2.

Department of Medical Biophysics, University of Toronto, Canada; Department of Imaging Research, Sunnybrook Research Institute, Toronto, Canada.

Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spatially and temporally inter-connected regions of enhancements, so they can be modeled as networks and their properties characterized via network-based connectivity. In this work, we represented nonmass lesions as graphs using a link formation energy model that favors linkages between regions of similar enhancement and closer spatial proximity. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183025
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http://dx.doi.org/10.1016/j.media.2018.10.011DOI Listing
January 2019
7 Reads
3.654 Impact Factor

A collaborative computer aided diagnosis (C-CAD) system with eye-tracking, sparse attentional model, and deep learning.

Med Image Anal 2019 Jan 28;51:101-115. Epub 2018 Oct 28.

Center for Research in Computer Vision, University of Central Florida, FL, United States. Electronic address:

Computer aided diagnosis (CAD) tools help radiologists to reduce diagnostic errors such as missing tumors and misdiagnosis. Vision researchers have been analyzing behaviors of radiologists during screening to understand how and why they miss tumors or misdiagnose. In this regard, eye-trackers have been instrumental in understanding visual search processes of radiologists. Read More

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http://dx.doi.org/10.1016/j.media.2018.10.010DOI Listing
January 2019
11 Reads

3D regression neural network for the quantification of enlarged perivascular spaces in brain MRI.

Med Image Anal 2019 Jan 26;51:89-100. Epub 2018 Oct 26.

Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC - University Medical Center Rotterdam, The Netherland; Image Group, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark. Electronic address:

Enlarged perivascular spaces (EPVS) in the brain are an emerging imaging marker for cerebral small vessel disease, and have been shown to be related to increased risk of various neurological diseases, including stroke and dementia. Automated quantification of EPVS would greatly help to advance research into its etiology and its potential as a risk indicator of disease. We propose a convolutional network regression method to quantify the extent of EPVS in the basal ganglia from 3D brain MRI. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183085
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http://dx.doi.org/10.1016/j.media.2018.10.008DOI Listing
January 2019
12 Reads

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.

Med Image Anal 2019 Jan 19;51:61-88. Epub 2018 Oct 19.

BCN MedTech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain; ICREA, Barcelona, Spain.

Fetal imaging is a burgeoning topic. New advancements in both magnetic resonance imaging and (3D) ultrasound currently allow doctors to diagnose fetal structural abnormalities such as those involved in twin-to-twin transfusion syndrome, gestational diabetes mellitus, pulmonary sequestration and hypoplasia, congenital heart disease, diaphragmatic hernia, ventriculomegaly, etc. Considering the continued breakthroughs in utero image analysis and (3D) reconstruction models, it is now possible to gain more insight into the ongoing development of the fetus. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183084
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http://dx.doi.org/10.1016/j.media.2018.10.003DOI Listing
January 2019
10 Reads

Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers.

Med Image Anal 2019 Jan 19;51:21-45. Epub 2018 Oct 19.

Department of Engineering Design, Indian Institute of Technology Madras, Chennai, India. Electronic address:

Deep fully convolutional neural network (FCN) based architectures have shown great potential in medical image segmentation. However, such architectures usually have millions of parameters and inadequate number of training samples leading to over-fitting and poor generalization. In this paper, we present a novel DenseNet based FCN architecture for cardiac segmentation which is parameter and memory efficient. Read More

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http://dx.doi.org/10.1016/j.media.2018.10.004DOI Listing
January 2019
5 Reads

Coronary artery centerline extraction in cardiac CT angiography using a CNN-based orientation classifier.

Med Image Anal 2019 Jan 22;51:46-60. Epub 2018 Oct 22.

Image Sciences Institute, University Medical Center Utrecht & Utrecht University, Q.02.4.45, 3508, GA, Utrecht, P.O. Box 85500, The Netherlands. Electronic address:

Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. In this work, we propose an algorithm that extracts coronary artery centerlines in CCTA using a convolutional neural network (CNN). In the proposed method, a 3D dilated CNN is trained to predict the most likely direction and radius of an artery at any given point in a CCTA image based on a local image patch. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183084
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http://dx.doi.org/10.1016/j.media.2018.10.005DOI Listing
January 2019
14 Reads

Improvement of fully automated airway segmentation on volumetric computed tomographic images using a 2.5 dimensional convolutional neural net.

Med Image Anal 2019 Jan 19;51:13-20. Epub 2018 Oct 19.

Department of Convergence Medicine, Biomedical Engineering Research Center, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea; Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, 88 Olympic-Ro 43-Gil Songpa-Gu, Seoul 05505, South Korea. Electronic address:

We propose a novel airway segmentation method in volumetric chest computed tomography (CT) and evaluate its performance on multiple datasets. The segmentation is performed voxel-by-voxel by a 2.5D convolutional neural net (2. Read More

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http://dx.doi.org/10.1016/j.media.2018.10.006DOI Listing
January 2019
2 Reads

Mind the gap: Quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search.

Med Image Anal 2019 Jan 10;51:1-12. Epub 2018 Oct 10.

Physense, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.

Pulmonary vein isolation (PVI) is a common procedure for the treatment of atrial fibrillation (AF) since the initial trigger for AF frequently originates in the pulmonary veins. A successful isolation produces a continuous lesion (scar) completely encircling the veins that stops activation waves from propagating to the atrial body. Unfortunately, the encircling lesion is often incomplete, becoming a combination of scar and gaps of healthy tissue. Read More

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http://dx.doi.org/10.1016/j.media.2018.10.001DOI Listing
January 2019

Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.

Med Image Anal 2018 Dec 24;50:167-180. Epub 2018 Sep 24.

Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, BC, Canada.

Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features. Read More

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http://dx.doi.org/10.1016/j.media.2018.09.005DOI Listing
December 2018
4 Reads

A hybrid camera- and ultrasound-based approach for needle localization and tracking using a 3D motorized curvilinear ultrasound probe.

Med Image Anal 2018 Dec 3;50:145-166. Epub 2018 Oct 3.

Department of Computer Engineering, German Jordanian University, Amman, Jordan.

Three-dimensional (3D) motorized curvilinear ultrasound probes provide an effective, low-cost tool to guide needle interventions, but localizing and tracking the needle in 3D ultrasound volumes is often challenging. In this study, a new method is introduced to localize and track the needle using 3D motorized curvilinear ultrasound probes. In particular, a low-cost camera mounted on the probe is employed to estimate the needle axis. Read More

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http://dx.doi.org/10.1016/j.media.2018.09.006DOI Listing
December 2018

Joint registration and synthesis using a probabilistic model for alignment of MRI and histological sections.

Med Image Anal 2018 Dec 22;50:127-144. Epub 2018 Sep 22.

Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.

Nonlinear registration of 2D histological sections with corresponding slices of MRI data is a critical step of 3D histology reconstruction algorithms. This registration is difficult due to the large differences in image contrast and resolution, as well as the complex nonrigid deformations and artefacts produced when sectioning the sample and mounting it on the glass slide. It has been shown in brain MRI registration that better spatial alignment across modalities can be obtained by synthesising one modality from the other and then using intra-modality registration metrics, rather than by using information theory based metrics to solve the problem directly. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183069
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http://dx.doi.org/10.1016/j.media.2018.09.002DOI Listing
December 2018
1 Read

Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke.

Med Image Anal 2018 Dec 23;50:117-126. Epub 2018 Sep 23.

LARIS, UMR IRHS INRA, Université d'Angers 62 avenue Notre Dame du Lac, Angers 49000, France. Electronic address:

We address the medical image analysis issue of predicting the final lesion in stroke from early perfusion magnetic resonance imaging. The classical processing approach for the dynamical perfusion images consists in a temporal deconvolution to improve the temporal signals associated with each voxel before performing prediction. We demonstrate here the value of exploiting directly the raw perfusion data by encoding the local environment of each voxel as a spatio-temporal texture, with an observation scale larger than the voxel. Read More

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http://dx.doi.org/10.1016/j.media.2018.08.008DOI Listing
December 2018
7 Reads

A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT.

Med Image Anal 2018 Dec 15;50:106-116. Epub 2018 Sep 15.

Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas 75235, USA. Electronic address:

We developed a kernelled support tensor machine (KSTM)-based model with tumor tensors derived from pre-treatment PET and CT imaging as input to predict distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). The patient cohort included 110 early stage NSCLC patients treated with SBRT, 25 of whom experienced failure at distant sites. Three-dimensional tumor tensors were constructed and used as input for the KSTM-based classifier. Read More

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http://dx.doi.org/10.1016/j.media.2018.09.004DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237633PMC
December 2018
4 Reads

Quantitative 3D Analysis of Coronary Wall Morphology in Heart Transplant Patients: OCT-Assessed Cardiac Allograft Vasculopathy Progression.

Med Image Anal 2018 Dec 14;50:95-105. Epub 2018 Sep 14.

Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA. Electronic address:

Cardiac allograft vasculopathy (CAV) accounts for about 30% of all heart-transplant (HTx) patient deaths. For patients at high risk for CAV complications after HTx, therapy must be initiated early to be effective. Therefore, new phenotyping approaches are needed to identify such HTx patients at the earliest possible time. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183069
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http://dx.doi.org/10.1016/j.media.2018.09.003DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6237624PMC
December 2018
5 Reads

Direct delineation of myocardial infarction without contrast agents using a joint motion feature learning architecture.

Med Image Anal 2018 Dec 6;50:82-94. Epub 2018 Sep 6.

Department of Medical Imaging, Western University, London ON, Canada.

Changes in mechanical properties of myocardium caused by a infarction can lead to kinematic abnormalities. This phenomenon has inspired us to develop this work for delineation of myocardial infarction area directly from non-contrast agents cardiac MR imaging sequences. The main contribution of this work is to develop a new joint motion feature learning architecture to efficiently establish direct correspondences between motion features and tissue properties. Read More

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http://dx.doi.org/10.1016/j.media.2018.09.001DOI Listing
December 2018
2 Reads
3.654 Impact Factor