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convolutional neural network

R Guerrero, C Qin, O Oktay, C Bowles, L Chen, R Joules, R Wolz, M C Valdés-Hernández, D A Dickie, J Wardlaw, D Rueckert
White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e...
2018: NeuroImage: Clinical
Alexander Tack, Anirban Mukhopadhyay, Stefan Zachow
OBJECTIVE: To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof. METHOD: A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain...
March 8, 2018: Osteoarthritis and Cartilage
Refael Vivanti, Leo Joskowicz, Naama Lev-Cohain, Ariel Ephrat, Jacob Sosna
Radiological longitudinal follow-up of tumors in CT scans is essential for disease assessment and liver tumor therapy. Currently, most tumor size measurements follow the RECIST guidelines, which can be off by as much as 50%. True volumetric measurements are more accurate but require manual delineation, which is time-consuming and user-dependent. We present a convolutional neural networks (CNN) based method for robust automatic liver tumor delineation in longitudinal CT studies that uses both global and patient specific CNNs trained on a small database of delineated images...
March 10, 2018: Medical & Biological Engineering & Computing
Muhammad Febrian Rachmadi, Maria Del C Valdés-Hernández, Maria Leonora Fatimah Agan, Carol Di Perri, Taku Komura
We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI...
February 17, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
Zhong Chen, Yifei Zhang, Chao Ouyang, Feng Zhang, Jie Ma
Landslides that take place in mountain cities tend to cause huge casualties and economic losses, and a precise survey of landslide areas is a critical task for disaster emergency. However, because of the complicated appearance of the nature, it is difficult to find a spatial regularity that only relates to landslides, thus landslides detection based on only spatial information or artificial features usually performs poorly. In this paper, an automated landslides detection approach that is aiming at mountain cities has been proposed based on pre- and post-event remote sensing images, it mainly utilizes the knowledge of landslide-related surface covering changes, and makes full use of the temporal and spatial information...
March 9, 2018: Sensors
Saumya S Gurbani, Eduard Schreibmann, Andrew A Maudsley, James Scott Cordova, Brian J Soher, Harish Poptani, Gaurav Verma, Peter B Barker, Hyunsuk Shim, Lee A D Cooper
PURPOSE: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viable imaging modality for studying several neuropathologies. However, a key hurdle in the routine clinical adoption of MRSI is the presence of spectral artifacts that can arise from a number of sources, possibly leading to false information...
March 9, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
Tatiana Fountoukidou, Philippe Raisin, Daniel Kaufmann, Jörn Justiz, Raphael Sznitman, Sebastian Wolf
PURPOSE: Selective retina therapy (SRT) is a laser treatment targeting specific posterior retinal layers. It is focused on inducing damage to the retinal pigment epithelium (RPE), while sparing other retinal tissue compared to traditional photocoagulation. However, the targeted RPE layer is invisible with most imaging modalities and induced SRT lesions cannot be monitored. In this work, imaging scans acquired from an experimental setup that couples the SRT laser beam with an optical coherence tomography (OCT) beam are analyzed in order to evaluate the treatment as they occur...
March 8, 2018: International Journal of Computer Assisted Radiology and Surgery
Chunfeng Lian, Jun Zhang, Mingxia Liu, Xiaopeng Zong, Sheng-Che Hung, Weili Lin, Dinggang Shen
Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is challenging and time-consuming for manual delineation of PVSs. Although several automatic/semi-automatic methods, especially the traditional learning-based approaches, have been proposed for segmentation of 3D PVSs, their performance often depends on the hand-crafted image features, as well as sophisticated preprocessing operations prior to segmentation (e...
February 27, 2018: Medical Image Analysis
Oisin Mac Aodha, Rory Gibb, Kate E Barlow, Ella Browning, Michael Firman, Robin Freeman, Briana Harder, Libby Kinsey, Gary R Mead, Stuart E Newson, Ivan Pandourski, Stuart Parsons, Jon Russ, Abigel Szodoray-Paradi, Farkas Szodoray-Paradi, Elena Tilova, Mark Girolami, Gabriel Brostow, Kate E Jones
Passive acoustic sensing has emerged as a powerful tool for quantifying anthropogenic impacts on biodiversity, especially for echolocating bat species. To better assess bat population trends there is a critical need for accurate, reliable, and open source tools that allow the detection and classification of bat calls in large collections of audio recordings. The majority of existing tools are commercial or have focused on the species classification task, neglecting the important problem of first localizing echolocation calls in audio which is particularly problematic in noisy recordings...
March 2018: PLoS Computational Biology
Luis A Camuñas-Mesa, Yaisel L Domínguez-Cordero, Alejandro Linares-Barranco, Teresa Serrano-Gotarredona, Bernabé Linares-Barranco
Convolutional Neural Networks (ConvNets) are a particular type of neural network often used for many applications like image recognition, video analysis or natural language processing. They are inspired by the human brain, following a specific organization of the connectivity pattern between layers of neurons known as receptive field. These networks have been traditionally implemented in software, but they are becoming more computationally expensive as they scale up, having limitations for real-time processing of high-speed stimuli...
2018: Frontiers in Neuroscience
Chanki Yu, Sejung Yang, Wonoh Kim, Jinwoong Jung, Kee-Yang Chung, Sang Wook Lee, Byungho Oh
BACKGROUND/PURPOSE: Acral melanoma is the most common type of melanoma in Asians, and usually results in a poor prognosis due to late diagnosis. We applied a convolutional neural network to dermoscopy images of acral melanoma and benign nevi on the hands and feet and evaluated its usefulness for the early diagnosis of these conditions. METHODS: A total of 724 dermoscopy images comprising acral melanoma (350 images from 81 patients) and benign nevi (374 images from 194 patients), and confirmed by histopathological examination, were analyzed in this study...
2018: PloS One
Gabriel Efrain Humpire Mamani, Arnaud Arindra Adiyoso Setio, Bram van Ginneken, Colin Jacobs
Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient method for simultaneous localization of multiple structures in 3D thorax-abdomen CT scans. 
 Our approach predicts the location of multiple structures using a single multi-label convolutional neural network for each orthogonal view. Each network takes extra slices around the current slice as input to provide extra context...
March 7, 2018: Physics in Medicine and Biology
Cosmas Mwikirize, John L Nosher, Ilker Hacihaliloglu
PURPOSE: We propose a framework for automatic and accurate detection of steeply inserted needles in 2D ultrasound data using convolution neural networks. We demonstrate its application in needle trajectory estimation and tip localization. METHODS: Our approach consists of a unified network, comprising a fully convolutional network (FCN) and a fast region-based convolutional neural network (R-CNN). The FCN proposes candidate regions, which are then fed to a fast R-CNN for finer needle detection...
March 6, 2018: International Journal of Computer Assisted Radiology and Surgery
Baojun Zhao, Boya Zhao, Linbo Tang, Yuqi Han, Wenzheng Wang
With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks...
March 4, 2018: Sensors
Lei Wang, Xin Xu, Hao Dong, Rong Gui, Fangling Pu
Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification methods can only classify one pixel each time. Because all the pixels of a PolSAR image are classified independently, the inherent interrelation of different land covers is ignored. We use a fixed-feature-size CNN (FFS-CNN) to classify all pixels in a patch simultaneously...
March 3, 2018: Sensors
Xiaofeng Du, Xiaobo Qu, Yifan He, Di Guo
Deep convolutional neural networks (CNNs) are successful in single-image super-resolution. Traditional CNNs are limited to exploit multi-scale contextual information for image reconstruction due to the fixed convolutional kernel in their building modules. To restore various scales of image details, we enhance the multi-scale inference capability of CNNs by introducing competition among multi-scale convolutional filters, and build up a shallow network under limited computational resources. The proposed network has the following two advantages: (1) the multi-scale convolutional kernel provides the multi-context for image super-resolution, and (2) the maximum competitive strategy adaptively chooses the optimal scale of information for image reconstruction...
March 6, 2018: Sensors
Han Byul Kim, Woong Woo Lee, Aryun Kim, Hong Ji Lee, Hye Young Park, Hyo Seon Jeon, Sang Kyong Kim, Beomseok Jeon, Kwang S Park
Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the use of a convolutional neural network (CNN) to differentiate the severity of symptoms as measured in data collected from a wearable device. Tremor signals were recorded from 92 PD patients using a custom-developed device (SNUMAP) equipped with an accelerometer and gyroscope mounted on a wrist module...
February 15, 2018: Computers in Biology and Medicine
Hongsheng Jin, Zongyao Li, Ruofeng Tong, Lanfen Lin
PURPOSE: The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false positive reduction plays a significant role in the detection. In this paper, we focus on the false positive reduction task and propose an effective method for this task. METHODS: We construct a deep 3D residual CNN (convolution neural network) to reduce false positive nodules from candidate nodules. The proposed network is much deeper than the traditional 3D CNNs used in medical image processing...
March 3, 2018: Medical Physics
Ana I L Namburete, Weidi Xie, Mohammad Yaqub, Andrew Zisserman, J Alison Noble
Methods for aligning 3D fetal neurosonography images must be robust to (i) intensity variations, (ii) anatomical and age-specific differences within the fetal population, and (iii) the variations in fetal position. To this end, we propose a multi-task fully convolutional neural network (FCN) architecture to address the problem of 3D fetal brain localization, structural segmentation, and alignment to a referential coordinate system. Instead of treating these tasks as independent problems, we optimize the network by simultaneously learning features shared within the input data pertaining to the correlated tasks, and later branching out into task-specific output streams...
February 21, 2018: Medical Image Analysis
Liang Lu, Lin Qi, Yisong Luo, Hengchao Jiao, Junyu Dong
Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN) instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects...
March 2, 2018: Sensors
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