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https://www.readbyqxmd.com/read/29774599/deep-convolutional-neural-network-for-segmentation-of-knee-joint-anatomy
#1
Zhaoye Zhou, Gengyan Zhao, Richard Kijowski, Fang Liu
PURPOSE: To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation. METHODS: A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures...
May 17, 2018: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/29772666/weed-growth-stage-estimator-using-deep-convolutional-neural-networks
#2
Nima Teimouri, Mads Dyrmann, Per Rydahl Nielsen, Solvejg Kopp Mathiassen, Gayle J Somerville, Rasmus Nyholm Jørgensen
This study outlines a new method of automatically estimating weed species and growth stages (from cotyledon until eight leaves are visible) of in situ images covering 18 weed species or families. Images of weeds growing within a variety of crops were gathered across variable environmental conditions with regards to soil types, resolution and light settings. Then, 9649 of these images were used for training the computer, which automatically divided the weeds into nine growth classes. The performance of this proposed convolutional neural network approach was evaluated on a further set of 2516 images, which also varied in term of crop, soil type, image resolution and light conditions...
May 16, 2018: Sensors
https://www.readbyqxmd.com/read/29772101/computer-aided-diagnosis-of-prostate-cancer-on-magnetic-resonance-imaging-using-a-convolutional-neural-network-algorithm
#3
Junichiro Ishioka, Yoh Matsuoka, Sho Uehara, Yosuke Yasuda, Toshiki Kijima, Soichiro Yoshida, Minato Yokoyama, Kazutaka Saito, Kazunori Kihara, Noboru Numao, Tomo Kimura, Kosei Kudo, Itsuo Kumazawa, Yasuhisa Fujii
OBJECTIVES: To develop a computer-aided diagnosis (CAD) algorithm with a deep learning architecture for detecting prostate cancer on magnetic resonance imaging (MRI) to promote global standardization and diminish variation in the interpretation of prostate MRI. PATIENTS AND METHODS: We retrospectively reviewed data from 335 patients with a prostate specific antigen level of less than 20 ng/ml who underwent MRI and extended systematic prostate biopsy with or without MRI-targeted biopsy...
May 17, 2018: BJU International
https://www.readbyqxmd.com/read/29770240/diagnosis-and-prediction-of-periodontally-compromised-teeth-using-a-deep-learning-based-convolutional-neural-network-algorithm
#4
Jae-Hong Lee, Do-Hyung Kim, Seong-Nyum Jeong, Seong-Ho Choi
Purpose: The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Methods: Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights...
April 2018: Journal of Periodontal & Implant Science
https://www.readbyqxmd.com/read/29769044/visualizing-histopathologic-deep-learning-classification-and-anomaly-detection-using-nonlinear-feature-space-dimensionality-reduction
#5
Kevin Faust, Quin Xie, Dominick Han, Kartikay Goyle, Zoya Volynskaya, Ugljesa Djuric, Phedias Diamandis
BACKGROUND: There is growing interest in utilizing artificial intelligence, and particularly deep learning, for computer vision in histopathology. While accumulating studies highlight expert-level performance of convolutional neural networks (CNNs) on focused classification tasks, most studies rely on probability distribution scores with empirically defined cutoff values based on post-hoc analysis. More generalizable tools that allow humans to visualize histology-based deep learning inferences and decision making are scarce...
May 16, 2018: BMC Bioinformatics
https://www.readbyqxmd.com/read/29768415/automated-and-real-time-segmentation-of-suspicious-breast-masses-using-convolutional-neural-network
#6
Viksit Kumar, Jeremy M Webb, Adriana Gregory, Max Denis, Duane D Meixner, Mahdi Bayat, Dana H Whaley, Mostafa Fatemi, Azra Alizad
In this work, a computer-aided tool for detection was developed to segment breast masses from clinical ultrasound (US) scans. The underlying Multi U-net algorithm is based on convolutional neural networks. Under the Mayo Clinic Institutional Review Board protocol, a prospective study of the automatic segmentation of suspicious breast masses was performed. The cohort consisted of 258 female patients who were clinically identified with suspicious breast masses and underwent clinical US scan and breast biopsy...
2018: PloS One
https://www.readbyqxmd.com/read/29766373/prostate-segmentation-in-mri-using-a-convolutional-neural-network-architecture-and-training-strategy-based-on-statistical-shape-models
#7
Davood Karimi, Golnoosh Samei, Claudia Kesch, Guy Nir, Septimiu E Salcudean
PURPOSE: Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues...
May 15, 2018: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/29763997/technical-note-deep-learning-based-mrac-using-rapid-ultra-short-echo-time-imaging
#8
Hyungseok Jang, Fang Liu, Gengyan Zhao, Tyler Bradshaw, Alan B McMillan
PURPOSE: In this study, we explore the feasibility of a novel framework for MR-based attenuation correction for PET/MR imaging based on deep learning via convolutional neural networks, which enables fully automated and robust estimation of a pseudo CT image based on ultrashort echo time (UTE), fat, and water images obtained by a rapid MR acquisition. METHODS: MR images for MRAC are acquired using dual echo ramped hybrid encoding (dRHE), where both UTE and out-of-phase echo images are obtained within a short single acquisition (35 sec)...
May 15, 2018: Medical Physics
https://www.readbyqxmd.com/read/29763743/affect-recognition-from-facial-movements-and-body-gestures-by-hierarchical-deep-spatio-temporal-features-and-fusion-strategy
#9
Bo Sun, Siming Cao, Jun He, Lejun Yu
Affect presentation is periodic and multi-modal, such as through facial movements, body gestures, and so on. Studies have shown that temporal selection and multi-modal combinations may benefit affect recognition. In this article, we therefore propose a spatio-temporal fusion model that extracts spatio-temporal hierarchical features based on select expressive components. In addition, a multi-modal hierarchical fusion strategy is presented. Our model learns the spatio-temporal hierarchical features from videos by a proposed deep network, which combines a convolutional neural networks (CNN), bilateral long short-term memory recurrent neural networks (BLSTM-RNN) with principal component analysis (PCA)...
December 7, 2017: Neural Networks: the Official Journal of the International Neural Network Society
https://www.readbyqxmd.com/read/29762901/cell-dynamic-morphology-classification-using-deep-convolutional-neural-networks
#10
Heng Li, Fengqian Pang, Yonggang Shi, Zhiwen Liu
Cell morphology is often used as a proxy measurement of cell status to understand cell physiology. Hence, interpretation of cell dynamic morphology is a meaningful task in biomedical research. Inspired by the recent success of deep learning, we here explore the application of convolutional neural networks (CNNs) to cell dynamic morphology classification. An innovative strategy for the implementation of CNNs is introduced in this study. Mouse lymphocytes were collected to observe the dynamic morphology, and two datasets were thus set up to investigate the performances of CNNs...
May 15, 2018: Cytometry. Part A: the Journal of the International Society for Analytical Cytology
https://www.readbyqxmd.com/read/29761358/deep-learning-for-staging-liver-fibrosis-on-ct-a-pilot-study
#11
Koichiro Yasaka, Hiroyuki Akai, Akira Kunimatsu, Osamu Abe, Shigeru Kiryu
OBJECTIVES: To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images. METHODS: This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference...
May 14, 2018: European Radiology
https://www.readbyqxmd.com/read/29760397/automatic-anatomical-classification-of-esophagogastroduodenoscopy-images-using-deep-convolutional-neural-networks
#12
Hirotoshi Takiyama, Tsuyoshi Ozawa, Soichiro Ishihara, Mitsuhiro Fujishiro, Satoki Shichijo, Shuhei Nomura, Motoi Miura, Tomohiro Tada
The use of convolutional neural networks (CNNs) has dramatically advanced our ability to recognize images with machine learning methods. We aimed to construct a CNN that could recognize the anatomical location of esophagogastroduodenoscopy (EGD) images in an appropriate manner. A CNN-based diagnostic program was constructed based on GoogLeNet architecture, and was trained with 27,335 EGD images that were categorized into four major anatomical locations (larynx, esophagus, stomach and duodenum) and three subsequent sub-classifications for stomach images (upper, middle, and lower regions)...
May 14, 2018: Scientific Reports
https://www.readbyqxmd.com/read/29758457/progress-and-remaining-challenges-in-high-throughput-volume-electron-microscopy
#13
REVIEW
Jörgen Kornfeld, Winfried Denk
Recent advances in the effectiveness of the automatic extraction of neural circuits from volume electron microscopy data have made us more optimistic that the goal of reconstructing the nervous system of an entire adult mammal (or bird) brain can be achieved in the next decade. The progress on the data analysis side-based mostly on variants of convolutional neural networks-has been particularly impressive, but improvements in the quality and spatial extent of published VEM datasets are substantial. Methodologically, the combination of hot-knife sample partitioning and ion milling stands out as a conceptual advance while the multi-beam scanning electron microscope promises to remove the data-acquisition bottleneck...
May 11, 2018: Current Opinion in Neurobiology
https://www.readbyqxmd.com/read/29758455/deep-neural-networks-for-automatic-detection-of-osteoporotic-vertebral-fractures-on-ct-scans
#14
Naofumi Tomita, Yvonne Y Cheung, Saeed Hassanpour
Osteoporotic vertebral fractures (OVFs) are prevalent in older adults and are associated with substantial personal suffering and socio-economic burden. Early diagnosis and treatment of OVFs are critical to prevent further fractures and morbidity. However, OVFs are often under-diagnosed and under-reported in computed tomography (CT) exams as they can be asymptomatic at an early stage. In this paper, we present and evaluate an automatic system that can detect incidental OVFs in chest, abdomen, and pelvis CT examinations at the level of practicing radiologists...
May 8, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29757739/multi-task-convolutional-neural-network-for-pose-invariant-face-recognition
#15
Xi Yin, Xiaoming Liu
This paper explores multi-task learning (MTL) for face recognition. First, we propose a multi-task convolutional neural network (CNN) for face recognition, where identity classification is the main task and pose, illumination, and expression (PIE) estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weights to each side task, which solves the crucial problem of balancing between different tasks in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses in a joint framework...
February 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/29757353/development-and-evaluation-of-a-deep-learning-model-for-protein-ligand-binding-affinity-prediction
#16
Marta M Stepniewska-Dziubinska, Piotr Zielenkiewicz, Pawel Siedlecki
Motivation: Structure based ligand discovery is one of the most successful approaches for augmenting the drug discovery process. Currently, there is a notable shift towards machine learning (ML) methodologies to aid such procedures. Deep learning has recently gained considerable attention as it allows the model to "learn" to extract features that are relevant for the task at hand. Results: We have developed a novel deep neural network estimating the binding affinity of ligand-receptor complexes...
May 10, 2018: Bioinformatics
https://www.readbyqxmd.com/read/29757338/automated-segmentation-of-the-choroid-in-edi-oct-images-with-retinal-pathology-using-convolution-neural-networks
#17
Min Chen, Jiancong Wang, Ipek Oguz, Brian L VanderBeek, James C Gee
The choroid plays a critical role in maintaining the portions of the eye responsible for vision. Specific alterations in the choroid have been associated with several disease states, including age-related macular degeneration (AMD), central serous choroiretinopathy, retinitis pigmentosa and diabetes. In addition, choroid thickness measures have been shown as a predictive biomarker for treatment response and visual function. Where several approaches currently exist for segmenting the choroid in optical coherence tomography (OCT) images of healthy retina, very few are capable of addressing images with retinal pathology...
September 2017: Fetal, infant and ophthalmic medical image analysis: International Workshop, FIFI 2017, and 4th International Workshop, OMIA 2017, held in conjunction with MICCAI 2017, Québec City, QC, Canada, September 14, Proceedings
https://www.readbyqxmd.com/read/29757211/convolutional-neural-network-based-embarrassing-situation-detection-under-camera-for-social-robot-in-smart-homes
#18
Guanci Yang, Jing Yang, Weihua Sheng, Francisco Erivaldo Fernandes Junior, Shaobo Li
Recent research has shown that the ubiquitous use of cameras and voice monitoring equipment in a home environment can raise privacy concerns and affect human mental health. This can be a major obstacle to the deployment of smart home systems for elderly or disabled care. This study uses a social robot to detect embarrassing situations. Firstly, we designed an improved neural network structure based on the You Only Look Once (YOLO) model to obtain feature information. By focusing on reducing area redundancy and computation time, we proposed a bounding-box merging algorithm based on region proposal networks (B-RPN), to merge the areas that have similar features and determine the borders of the bounding box...
May 12, 2018: Sensors
https://www.readbyqxmd.com/read/29756129/deep-multi-task-multi-channel-learning-for-joint-classification-and-regression-of-brain-status
#19
Mingxia Liu, Jun Zhang, Ehsan Adeli, Dinggang Shen
Jointly identifying brain diseases and predicting clinical scores have attracted increasing attention in the domain of computer-aided diagnosis using magnetic resonance imaging (MRI) data, since these two tasks are highly correlated. Although several joint learning models have been developed, most existing methods focus on using human-engineered features extracted from MRI data. Due to the possible heterogeneous property between human-engineered features and subsequent classification/regression models, those methods may lead to sub-optimal learning performance...
September 2017: Medical Image Computing and Computer-assisted Intervention: MICCAI ..
https://www.readbyqxmd.com/read/29755716/automatic-semantic-segmentation-of-brain-gliomas-from-mri-images-using-a-deep-cascaded-neural-network
#20
Shaoguo Cui, Lei Mao, Jingfeng Jiang, Chang Liu, Shuyu Xiong
Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure...
2018: Journal of Healthcare Engineering
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