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Deep Learning MRI

Fan Li, Manhua Liu
Alzheimer's disease (AD) is an irreversible neurodegenerative disorder with progressive impairment of memory and cognitive functions. Structural magnetic resonance images (MRI) play important role to evaluate the brain anatomical changes for AD Diagnosis. Machine learning technologies have been widely studied on MRI computation and analysis for quantitative evaluation and computer-aided-diagnosis of AD. Most existing methods extract the hand-craft features after image processing such as registration and segmentation, and then train a classifier to distinguish AD subjects from other groups...
October 2, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Yujing Zhou, Yuan Liu, Qian Chen, Guohua Gu, Xiubao Sui
The aim of this research is to automatically detect lumbar vertebras in MRI images with bounding boxes and their classes, which can assist clinicians with diagnoses based on large amounts of MRI slices. Vertebras are highly semblable in appearance, leading to a challenging automatic recognition. A novel detection algorithm is proposed in this paper based on deep learning. We apply a similarity function to train the convolutional network for lumbar spine detection. Instead of distinguishing vertebras using annotated lumbar images, our method compares similarities between vertebras using a beforehand lumbar image...
October 18, 2018: Journal of Digital Imaging: the Official Journal of the Society for Computer Applications in Radiology
Jose Dolz, Xiaopan Xu, Jérôme Rony, Jing Yuan, Yang Liu, Eric Granger, Christian Desrosiers, Xi Zhang, Ismail Ben Ayed, Hongbing Lu
PURPOSE: Precise segmentation of bladder walls and tumor regions is an essential step towards non-invasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC). However, the automatic delineation of bladder walls and tumor in magnetic resonance images (MRI) is a challenging task, due to important bladder shape variations, strong intensity inhomogeneity in urine and very high variability across population, particularly on tumors appearance...
October 17, 2018: Medical Physics
Tanguy Duval, Ariane Saliani, Harris Nami, Antonio Nanci, Nikola Stikov, Hugues Leblond, Julien Cohen-Adad
Due to the technical challenges of large-scale microscopy and analysis, to date only limited knowledge has been made available about axon morphometry (diameter, shape, myelin thickness, volume fraction), thereby limiting our understanding of neuronal microstructure and slowing down research on neurodegenerative pathologies. This study addresses this knowledge gap by establishing a state-of-the-art acquisition and analysis framework for mapping axon morphometry, and providing the first comprehensive mapping of axon morphometry in the human spinal cord...
October 13, 2018: NeuroImage
Florian Dubost, Pinar Yilmaz, Hieab Adams, Gerda Bortsova, M Arfan Ikram, Wiro Niessen, Meike Vernooij, Marleen de Bruijne
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, are common in aging, and are considered a reflection of cerebral small vessel disease. As such, assessing the burden of PVS has promise as a brain imaging marker. Visual and manual scoring of PVS is a tedious and observer-dependent task. Automated methods would advance research into the etiology of PVS, could aid to assess what a "normal" burden is in aging, and could evaluate the potential of PVS as a biomarker of cerebral small vessel disease...
October 13, 2018: NeuroImage
Hongming Li, Yong Fan
Decoding brain functional states underlying different cognitive processes using multivariate pattern recognition techniques has attracted increasing interests in brain imaging studies. Promising performance has been achieved using brain functional connectivity or brain activation signatures for a variety of brain decoding tasks. However, most of existing studies have built decoding models upon features extracted from imaging data at individual time points or temporal windows with a fixed interval, which might not be optimal across different cognitive processes due to varying temporal durations and dependency of different cognitive processes...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Hongming Li, Yong Fan
Dynamic functional connectivity analysis provides valuable information for understanding brain functional activity underlying different cognitive processes. Besides sliding window based approaches, a variety of methods have been developed to automatically split the entire functional MRI scan into segments by detecting change points of functional signals to facilitate better characterization of temporally dynamic functional connectivity patterns. However, these methods are based on certain assumptions for the functional signals, such as Gaussian distribution, which are not necessarily suitable for the fMRI data...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Hao Yang, Junran Zhang, Qihong Liu, Yi Wang
BACKGROUND: Recently, deep learning technologies have rapidly expanded into medical image analysis, including both disease detection and classification. As far as we know, migraine is a disabling and common neurological disorder, typically characterized by unilateral, throbbing and pulsating headaches. Unfortunately, a large number of migraineurs do not receive the accurate diagnosis when using traditional diagnostic criteria based on the guidelines of the International Headache Society...
October 11, 2018: Biomedical Engineering Online
Dong Nie, Li Wang, Yaozong Gao, Jun Lian, Dinggang Shen
Accurate segmentation of pelvic organs is important for prostate radiation therapy. Modern radiation therapy starts to use a magnetic resonance image (MRI) as an alternative to computed tomography image because of its superior soft tissue contrast and also free of risk from radiation exposure. However, segmentation of pelvic organs from MRI is a challenging problem due to inconsistent organ appearance across patients and also large intrapatient anatomical variations across treatment days. To address such challenges, we propose a novel deep network architecture, called ``Spatially varying sTochastic Residual AdversarIal Network'' (STRAINet), to delineate pelvic organs from MRI in an end-to-end fashion...
October 9, 2018: IEEE Transactions on Neural Networks and Learning Systems
Valentina Pedoia, Berk Norman, Sarah N Mehany, Matthew D Bucknor, Thomas M Link, Sharmila Majumdar
BACKGROUND: Semiquantitative assessment of MRI plays a central role in musculoskeletal research; however, in the clinical setting MRI reports often tend to be subjective and qualitative. Grading schemes utilized in research are not used because they are extraordinarily time-consuming and unfeasible in clinical practice. PURPOSE: To evaluate the ability of deep-learning models to detect and stage severity of meniscus and patellofemoral cartilage lesions in osteoarthritis and anterior cruciate ligament (ACL) subjects...
October 10, 2018: Journal of Magnetic Resonance Imaging: JMRI
Patrick M Colletti
No abstract text is available yet for this article.
October 9, 2018: Radiology
Qian Tao, Wenjun Yan, Yuanyuan Wang, Elisabeth H M Paiman, Denis P Shamonin, Pankaj Garg, Sven Plein, Lu Huang, Liming Xia, Marek Sramko, Jarsolav Tintera, Albert de Roos, Hildo J Lamb, Rob J van der Geest
Purpose To develop a deep learning-based method for fully automated quantification of left ventricular (LV) function from short-axis cine MR images and to evaluate its performance in a multivendor and multicenter setting. Materials and Methods This retrospective study included cine MRI data sets obtained from three major MRI vendors in four medical centers from 2008 to 2016. Three convolutional neural networks (CNNs) with the U-NET architecture were trained on data sets of increasing variability: (a) a single-vendor, single-center, homogeneous cohort of 100 patients (CNN1); (b) a single-vendor, multicenter, heterogeneous cohort of 200 patients (CNN2); and (c) a multivendor, multicenter, heterogeneous cohort of 400 patients (CNN3)...
October 9, 2018: Radiology
Imran Razzak, Muhammad Imran, Guandong Xu
Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of Deep learning such as Convolutional Neural Networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain...
October 4, 2018: IEEE Journal of Biomedical and Health Informatics
Jue Jiang, Yu-Chi Hu, Neelam Tyagi, Pengpeng Zhang, Andreas Rimner, Gig S Mageras, Joseph O Deasy, Harini Veeraraghavan
We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images...
September 2018: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
Fei Gao, Teresa Wu, Jing Li, Bin Zheng, Lingxiang Ruan, Desheng Shang, Bhavika Patel
Breast cancer is the second leading cause of cancer death among women worldwide. Nevertheless, it is also one of the most treatable malignances if detected early. Screening for breast cancer with full field digital mammography (FFDM) has been widely used. However, it demonstrates limited performance for women with dense breasts. An emerging technology in the field is contrast-enhanced digital mammography (CEDM), which includes a low energy (LE) image similar to FFDM, and a recombined image leveraging tumor neoangiogenesis similar to breast magnetic resonance imaging (MRI)...
September 22, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
H M Warren-Forward, O Kalthoff
INTRODUCTION: Using magnetic resonance imaging (MRI) safety as an example, this paper discusses the development of an innovative multiple-step assignment task designed to increase student engagement and learning of important concepts. The paper also summarises student feedback about the assessment as well as thematic analysis of categories thought important to students. METHOD: A multi-step assignment was designed. Step one was the reading of a MRI safety article, step two was the construction and submission of 5 short answer questions believed to be important concepts of understanding and step three was the answering of 15 questions compiled from all student questions by the course coordinator...
November 2018: Radiography
Mehmet Akçakaya, Steen Moeller, Sebastian Weingärtner, Kâmil Uğurbil
PURPOSE: To develop an improved k-space reconstruction method using scan-specific deep learning that is trained on autocalibration signal (ACS) data. THEORY: Robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction trains convolutional neural networks on ACS data. This enables nonlinear estimation of missing k-space lines from acquired k-space data with improved noise resilience, as opposed to conventional linear k-space interpolation-based methods, such as GRAPPA, which are based on linear convolutional kernels...
September 18, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Valentina Pedoia, Sharmila Majumdar
In an effort to develop quantitative biomarkers for degenerative joint disease and fill the void that exists for diagnosing, monitoring and assessing the extent of whole joint degeneration, the past decade has been marked by a greatly increased role of noninvasive imaging. This coupled with recent advances in image processing and deep learning opens new possibilities for promising quantitative techniques. The clinical translation of quantitative imaging was previously hampered by tedious non-scalable and subjective image analysis...
October 1, 2018: Journal of Orthopaedic Research: Official Publication of the Orthopaedic Research Society
Stefan Winzeck, Arsany Hakim, Richard McKinley, José A A D S R Pinto, Victor Alves, Carlos Silva, Maxim Pisov, Egor Krivov, Mikhail Belyaev, Miguel Monteiro, Arlindo Oliveira, Youngwon Choi, Myunghee Cho Paik, Yongchan Kwon, Hanbyul Lee, Beom Joon Kim, Joong-Ho Won, Mobarakol Islam, Hongliang Ren, David Robben, Paul Suetens, Enhao Gong, Yilin Niu, Junshen Xu, John M Pauly, Christian Lucas, Mattias P Heinrich, Luis C Rivera, Laura S Castillo, Laura A Daza, Andrew L Beers, Pablo Arbelaezs, Oskar Maier, Ken Chang, James M Brown, Jayashree Kalpathy-Cramer, Greg Zaharchuk, Roland Wiest, Mauricio Reyes
Performance of models highly depend not only on the used algorithm but also the data set it was applied to. This makes the comparison of newly developed tools to previously published approaches difficult. Either researchers need to implement others' algorithms first, to establish an adequate benchmark on their data, or a direct comparison of new and old techniques is infeasible. The Ischemic Stroke Lesion Segmentation (ISLES) challenge, which has ran now consecutively for 3 years, aims to address this problem of comparability...
2018: Frontiers in Neurology
Yabo Fu, Thomas R Mazur, Xue Wu, Shi Liu, Xiao Chang, Yonggang Lu, H Harold Li, Hyun Kim, Michael C Roach, Lauren Henke, Deshan Yang
PURPOSE: To expedite the contouring process for MRI-guided adaptive radiotherapy (MR-IGART), a convolutional neural network (CNN) deep-learning (DL) model is proposed to accurately segment the liver, kidneys, stomach, bowel and duodenum in 3D MR images. METHODS: Images and structure contours for 120 patients were collected retrospectively. Treatment sites included pancreas, liver, stomach, adrenal gland and prostate. The proposed DL model contains a voxel-wise label prediction CNN and a correction network which consists of two sub-networks...
September 30, 2018: Medical Physics
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