1,529 results match your criteria Medical image analysis[Journal]


Unsupervised tumor detection in Dynamic PET/CT imaging of the prostate.

Med Image Anal 2019 Apr 6;55:27-40. Epub 2019 Apr 6.

School of Computer Science, Tel Aviv University, Tel Aviv, Israel.

Early detection and localization of prostate tumors pose a challenge to the medical community. Several imaging techniques, including PET, have shown some success. But no robust and accurate solution has yet been reached. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183030
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http://dx.doi.org/10.1016/j.media.2019.04.001DOI Listing
April 2019
1 Read

Pulmonary nodule detection in CT scans with equivariant CNNs.

Med Image Anal 2019 Mar 28;55:15-26. Epub 2019 Mar 28.

University of Amsterdam, Netherlands.

Convolutional Neural Networks (CNNs) require a large amount of annotated data to learn from, which is often difficult to obtain for medical imaging problems. In this work we show that the sample complexity of CNNs can be significantly improved by using 3D roto-translation group convolutions instead of standard translational convolutions. 3D CNNs with group convolutions (3D G-CNNs) were applied to the problem of false positive reduction for pulmonary nodule detection in CT scans, and proved to be substantially more effective in terms of accuracy, sensitivity to malignant nodules, and speed of convergence compared to a strong and comparable baseline architecture with regular convolutions, extensive data augmentation and a similar number of parameters. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.010DOI Listing

Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features.

Med Image Anal 2019 Apr 4;55:1-14. Epub 2019 Apr 4.

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

Tumor segmentation in whole-slide images of histology slides is an important step towards computer-assisted diagnosis. In this work, we propose a tumor segmentation framework based on the novel concept of persistent homology profiles (PHPs). For a given image patch, the homology profiles are derived by efficient computation of persistent homology, which is an algebraic tool from homology theory. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.014DOI Listing
April 2019
1 Read

Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising.

Med Image Anal 2019 Mar 21;54:306-315. Epub 2019 Mar 21.

J. Crayton Pruitt Family Dept. of Biomedical Engineering, University of Florida, 1275 Center Drive, Gainesville, FL 32611 USA. Electronic address:

Deep convolutional neural networks offer state-of-the-art performance for medical image analysis. However, their architectures are manually designed for particular problems. On the one hand, a manual designing process requires many trials to tune a large number of hyperparameters and is thus quite a time-consuming task. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.004DOI Listing
March 2019
6 Reads

Graph Convolutions on Spectral Embeddings for Cortical Surface Parcellation.

Med Image Anal 2019 Mar 30;54:297-305. Epub 2019 Mar 30.

ETS Montreal, Computer and Software Engineering, 1100 Notre Dame St. W., Montreal, QC H3C 1K3, Canada.

Neuronal cell bodies mostly reside in the cerebral cortex. The study of this thin and highly convoluted surface is essential for understanding how the brain works. The analysis of surface data is, however, challenging due to the high variability of the cortical geometry. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.012DOI Listing

Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis.

Med Image Anal 2019 Mar 29;54:280-296. Epub 2019 Mar 29.

Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands.

Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods that can learn with less/other types of supervision, have been proposed. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.009DOI Listing

Fully automatic 3D reconstruction of the placenta and its peripheral vasculature in intrauterine fetal MRI.

Med Image Anal 2019 Mar 28;54:263-279. Epub 2019 Mar 28.

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

Recent advances in fetal magnetic resonance imaging (MRI) open the door to improved detection and characterization of fetal and placental abnormalities. Since interpreting MRI data can be complex and ambiguous, there is a need for robust computational methods able to quantify placental anatomy (including its vasculature) and function. In this work, we propose a novel fully-automated method to segment the placenta and its peripheral blood vessels from fetal MRI. Read More

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https://linkinghub.elsevier.com/retrieve/pii/S13618415183030
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http://dx.doi.org/10.1016/j.media.2019.03.008DOI Listing
March 2019
2 Reads

DeepPET: A deep encoder-decoder network for directly solving the PET image reconstruction inverse problem.

Med Image Anal 2019 Mar 30;54:253-262. Epub 2019 Mar 30.

Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, United States; Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, United States.

The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder-decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.013DOI Listing
March 2019
1 Read

Discovering hierarchical common brain networks via multimodal deep belief network.

Med Image Anal 2019 Mar 29;54:238-252. Epub 2019 Mar 29.

Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA. Electronic address:

Studying a common architecture reflecting both brain's structural and functional organizations across individuals and populations in a hierarchical way has been of significant interest in the brain mapping field. Recently, deep learning models exhibited ability in extracting meaningful hierarchical structures from brain imaging data, e.g. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.011DOI Listing
March 2019
1 Read
3.654 Impact Factor

A modality-adaptive method for segmenting brain tumors and organs-at-risk in radiation therapy planning.

Med Image Anal 2019 Mar 22;54:220-237. Epub 2019 Mar 22.

Department of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.005DOI Listing
March 2019
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Integrating spatial configuration into heatmap regression based CNNs for landmark localization.

Med Image Anal 2019 Mar 25;54:207-219. Epub 2019 Mar 25.

Ludwig Boltzmann Institute for Clinical Forensic Imaging, Graz, Austria; Medical University of Graz, BioTechMed-Graz, Austria. Electronic address:

In many medical image analysis applications, only a limited amount of training data is available due to the costs of image acquisition and the large manual annotation effort required from experts. Training recent state-of-the-art machine learning methods like convolutional neural networks (CNNs) from small datasets is a challenging task. In this work on anatomical landmark localization, we propose a CNN architecture that learns to split the localization task into two simpler sub-problems, reducing the overall need for large training datasets. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.007DOI Listing

BIRNet: Brain image registration using dual-supervised fully convolutional networks.

Med Image Anal 2019 Mar 22;54:193-206. Epub 2019 Mar 22.

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

In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for training can be challenging, we design a fully convolutional network that is subject to dual-guidance: (1) Ground-truth guidance using deformation fields obtained by an existing registration method; and (2) Image dissimilarity guidance using the difference between the images after registration. The latter guidance helps avoid overly relying on the supervision from the training deformation fields, which could be inaccurate. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.006DOI Listing
March 2019
3 Reads

Patient-attentive sequential strategy for perimetry-based visual field acquisition.

Med Image Anal 2019 Mar 23;54:179-192. Epub 2019 Mar 23.

ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

Perimetry is a non-invasive clinical psychometric examination used for diagnosing ophthalmic and neurological conditions. At its core, perimetry relies on a subject pressing a button whenever they see a visual stimulus within their field of view. This sequential process then yields a 2D visual field image that is critical for clinical use. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.002DOI Listing
March 2019
1 Read

CT male pelvic organ segmentation using fully convolutional networks with boundary sensitive representation.

Med Image Anal 2019 Mar 21;54:168-178. Epub 2019 Mar 21.

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

Accurate segmentation of the prostate and organs at risk (e.g., bladder and rectum) in CT images is a crucial step for radiation therapy in the treatment of prostate cancer. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.003DOI Listing
March 2019
6 Reads

Ultrasound guidance in minimally invasive robotic procedures.

Med Image Anal 2019 Jan 11;54:149-167. Epub 2019 Jan 11.

Institute of Health & Biomedical Innovation, Queensland University of Technology, Brisbane, Queensland, Australia; School of Clinical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia; Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht, the Netherlands. Electronic address:

In the past decade, medical robotics has gained significant traction within the surgical field. While the introduction of fully autonomous robotic systems for surgical procedures still remains a challenge, robotic assisted interventions have become increasingly more interesting for the scientific and clinical community. This happens especially when difficulties associated with complex surgical manoeuvres under reduced field of view are involved, as encountered in minimally invasive surgeries. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.002DOI Listing
January 2019
1 Read

Population shrinkage of covariance (PoSCE) for better individual brain functional-connectivity estimation.

Med Image Anal 2019 Mar 15;54:138-148. Epub 2019 Mar 15.

Parietal Team, INRIA/CEA, Paris-Saclay University, 1 rue Honoré d'Estienne d'Orves, Palaiseau, 91120, France. Electronic address:

Estimating covariances from functional Magnetic Resonance Imaging at rest (r-fMRI) can quantify interactions between brain regions. Also known as brain functional connectivity, it reflects inter-subject variations in behavior and cognition, and characterizes neuropathologies. Yet, with noisy and short time-series, as in r-fMRI, covariance estimation is challenging and calls for penalization, as with shrinkage approaches. Read More

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http://dx.doi.org/10.1016/j.media.2019.03.001DOI Listing

Exploiting structural redundancy in q-space for improved EAP reconstruction from highly undersampled (k, q)-space in DMRI.

Med Image Anal 2019 Mar 13;54:122-137. Epub 2019 Mar 13.

Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA. Electronic address:

Accurate reconstruction of the ensemble average propagators (EAPs) from undersampled diffusion MRI (dMRI) measurements is a well-motivated, actively researched problem in the field of dMRI acquisition and analysis. A number of approaches based on compressed sensing (CS) principles have been developed for this problem, achieving a considerable acceleration in the acquisition by leveraging sparse representations of the signal. Most recent methods in literature apply undersampling techniques in the (k, q)-space for the recovery of EAP in the joint (x, r)-space. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.014DOI Listing

Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge.

Med Image Anal 2019 Feb 27;54:111-121. Epub 2019 Feb 27.

Medical Image Analysis Group, Eindhoven University of Technology, Eindhoven, the Netherlands.

Tumor proliferation is an important biomarker indicative of the prognosis of breast cancer patients. Assessment of tumor proliferation in a clinical setting is a highly subjective and labor-intensive task. Previous efforts to automate tumor proliferation assessment by image analysis only focused on mitosis detection in predefined tumor regions. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.012DOI Listing
February 2019
2 Reads
3.654 Impact Factor

Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network.

Med Image Anal 2019 Feb 22;54:100-110. Epub 2019 Feb 22.

Simon Fraser University, School of Engineering Science, Burnaby V5A 1S6, Canada. Electronic address:

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. These images can help reveal disease-related alterations below the surface of the retina, such as the presence of edema, or accumulation of fluid which can distort vision, and are an indication of disruptions in the vasculature of the retina. In this paper, a new framework is proposed for multiclass fluid segmentation and detection in the retinal OCT images. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.011DOI Listing
February 2019

Constrained-CNN losses for weakly supervised segmentation.

Med Image Anal 2019 Feb 13;54:88-99. Epub 2019 Feb 13.

ÉTS Montréal, QC, Canada.

Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.009DOI Listing
February 2019
1 Read

Breast MRI and X-ray mammography registration using gradient values.

Med Image Anal 2019 Feb 26;54:76-87. Epub 2019 Feb 26.

Institute of Computer Vision and Robotics, University of Girona, Spain.

Breast magnetic resonance imaging (MRI) and X-ray mammography are two image modalities widely used for early detection and diagnosis of breast diseases in women. The combination of these modalities, traditionally done using intensity-based registration algorithms, leads to a more accurate diagnosis and treatment, due to the capability of co-localizing lesions and susceptibles areas between the two image modalities. In this work, we present the first attempt to register breast MRI and X-ray mammographic images using intensity gradients as the similarity measure. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.013DOI Listing
February 2019
2 Reads

Optimal surface segmentation with convex priors in irregularly sampled space.

Med Image Anal 2019 Feb 8;54:63-75. Epub 2019 Feb 8.

Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, USA; Department of Radiation Oncology, University of Iowa, Iowa City, IA, 52242, USA. Electronic address:

Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly distributed orthogonal voxels of the volume. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.004DOI Listing
February 2019
7 Reads

AAR-RT - A system for auto-contouring organs at risk on CT images for radiation therapy planning: Principles, design, and large-scale evaluation on head-and-neck and thoracic cancer cases.

Med Image Anal 2019 Jan 29;54:45-62. Epub 2019 Jan 29.

Medical Image Processing Group, Department of Radiology, University of Pennsylvania, 602 Goddard building, 3710 Hamilton Walk, 6th Floor, Rm 602W, Philadelphia, PA 19104, United States.

Contouring (segmentation) of Organs at Risk (OARs) in medical images is required for accurate radiation therapy (RT) planning. In current clinical practice, OAR contouring is performed with low levels of automation. Although several approaches have been proposed in the literature for improving automation, it is difficult to gain an understanding of how well these methods would perform in a realistic clinical setting. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.008DOI Listing
January 2019
2 Reads

f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks.

Med Image Anal 2019 Jan 31;54:30-44. Epub 2019 Jan 31.

Christian Doppler Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University Vienna, Austria.

Obtaining expert labels in clinical imaging is difficult since exhaustive annotation is time-consuming. Furthermore, not all possibly relevant markers may be known and sufficiently well described a priori to even guide annotation. While supervised learning yields good results if expert labeled training data is available, the visual variability, and thus the vocabulary of findings, we can detect and exploit, is limited to the annotated lesions. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.010DOI Listing
January 2019

An image interpolation approach for acquisition time reduction in navigator-Based 4D MRI.

Med Image Anal 2019 Feb 13;54:20-29. Epub 2019 Feb 13.

Biomedical Image Computing Group, ETH Zurich, Switzerland.

Navigated 2D multi-slice dynamic Magnetic Resonance (MR) imaging enables high contrast 4D MR imaging during free breathing and provides in-vivo observations for treatment planning and guidance. Navigator slices are vital for retrospective stacking of 2D data slices in this method. However, they also prolong the acquisition sessions. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.008DOI Listing
February 2019
1 Read

Medical image classification using synergic deep learning.

Med Image Anal 2019 Feb 18;54:10-19. Epub 2019 Feb 18.

National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China; Centre for Multidisciplinary Convergence Computing (CMCC), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an 710072, China. Electronic address:

The classification of medical images is an essential task in computer-aided diagnosis, medical image retrieval and mining. Although deep learning has shown proven advantages over traditional methods that rely on the handcrafted features, it remains challenging due to the significant intra-class variation and inter-class similarity caused by the diversity of imaging modalities and clinical pathologies. In this paper, we propose a synergic deep learning (SDL) model to address this issue by using multiple deep convolutional neural networks (DCNNs) simultaneously and enabling them to mutually learn from each other. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.010DOI Listing
February 2019

OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions.

Med Image Anal 2019 Feb 13;54:1-9. Epub 2019 Feb 13.

Institute of Medical Informatics, University of Lübeck, Germany.

Deep networks have set the state-of-the-art in most image analysis tasks by replacing handcrafted features with learned convolution filters within end-to-end trainable architectures. Still, the specifications of a convolutional network are subject to much manual design - the shape and size of the receptive field for convolutional operations is a very sensitive part that has to be tuned for different image analysis applications. 3D fully-convolutional multi-scale architectures with skip-connection that excel at semantic segmentation and landmark localisation have huge memory requirements and rely on large annotated datasets - an important limitation for wider adaptation in medical image analysis. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.006DOI Listing
February 2019
2 Reads

Attention gated networks: Learning to leverage salient regions in medical images.

Med Image Anal 2019 Apr 5;53:197-207. Epub 2019 Feb 5.

BioMedIA, Imperial College London, London, SW7 2AZ, UK.

We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). Read More

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http://dx.doi.org/10.1016/j.media.2019.01.012DOI Listing

Frequency-splitting dynamic MRI reconstruction using multi-scale 3D convolutional sparse coding and automatic parameter selection.

Med Image Anal 2019 Apr 7;53:179-196. Epub 2019 Feb 7.

School of Electrical and Computer Engineering, Ulsan National Institute of Science and Technology (UNIST), South Korea. Electronic address:

In this paper, we propose a novel image reconstruction algorithm using multi-scale 3D convolutional sparse coding and a spectral decomposition technique for highly undersampled dynamic Magnetic Resonance Imaging (MRI) data. The proposed method recovers high-frequency information using a shared 3D convolution-based dictionary built progressively during the reconstruction process in an unsupervised manner, while low-frequency information is recovered using a total variation-based energy minimization method that leverages temporal coherence in dynamic MRI. Additionally, the proposed 3D dictionary is built across three different scales to more efficiently adapt to various feature sizes, and elastic net regularization is employed to promote a better approximation to the sparse input data. Read More

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http://dx.doi.org/10.1016/j.media.2019.02.001DOI Listing

Weakly supervised mitosis detection in breast histopathology images using concentric loss.

Med Image Anal 2019 Apr 15;53:165-178. Epub 2019 Feb 15.

Tencent AI Lab, PR China; Department of CSE, University of Texas at Arlington, Arlington, US. Electronic address:

Developing new deep learning methods for medical image analysis is a prevalent research topic in machine learning. In this paper, we propose a deep learning scheme with a novel loss function for weakly supervised breast cancer diagnosis. According to the Nottingham Grading System, mitotic count plays an important role in breast cancer diagnosis and grading. Read More

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http://dx.doi.org/10.1016/j.media.2019.01.013DOI Listing
April 2019
2 Reads

Evaluating reinforcement learning agents for anatomical landmark detection.

Med Image Anal 2019 Apr 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
April 2019
4 Reads

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

Med Image Anal 2019 Apr 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
April 2019
1 Read

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

Med Image Anal 2019 Apr 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
April 2019
1 Read

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

Med Image Anal 2019 Apr 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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https://linkinghub.elsevier.com/retrieve/pii/S13618415183059
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http://dx.doi.org/10.1016/j.media.2019.01.011DOI Listing
April 2019
3 Reads

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

Med Image Anal 2019 Apr 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
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397780PMC
April 2019
1 Read

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

Med Image Anal 2019 Apr 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
April 2019
1 Read

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

Med Image Anal 2019 Apr 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
April 2019
1 Read

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

Med Image Anal 2019 Apr 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
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397790PMC
April 2019
2 Reads

Recurrent inference machines for reconstructing heterogeneous MRI data.

Med Image Anal 2019 Apr 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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https://linkinghub.elsevier.com/retrieve/pii/S13618415183060
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http://dx.doi.org/10.1016/j.media.2019.01.005DOI Listing
April 2019
16 Reads

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

Med Image Anal 2019 Apr 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
April 2019
14 Reads

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

Med Image Anal 2019 Apr 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
April 2019
9 Reads

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

Med Image Anal 2019 Apr 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
April 2019
3 Reads

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

Med Image Anal 2019 Apr 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
April 2019
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Repetitive motion compensation for real time intraoperative video processing.

Med Image Anal 2019 Apr 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
April 2019
9 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
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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
1 Read
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
2 Reads

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
3 Reads