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https://www.readbyqxmd.com/read/29775951/retinal-blood-vessel-segmentation-using-fully-convolutional-network-with-transfer-learning
#1
Zhexin Jiang, Hao Zhang, Yi Wang, Seok-Bum Ko
Since the retinal blood vessel has been acknowledged as an indispensable element in both ophthalmological and cardiovascular disease diagnosis, the accurate segmentation of the retinal vessel tree has become the prerequisite step for automated or computer-aided diagnosis systems. In this paper, a supervised method is presented based on a pre-trained fully convolutional network through transfer learning. This proposed method has simplified the typical retinal vessel segmentation problem from full-size image segmentation to regional vessel element recognition and result merging...
April 26, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29772101/computer-aided-diagnosis-of-prostate-cancer-on-magnetic-resonance-imaging-using-a-convolutional-neural-network-algorithm
#2
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/29770897/machine-learning-a-useful-radiological-adjunct-in-determination-of-a-newly-diagnosed-glioma-s-grade-and-idh-status
#3
Céline De Looze, Alan Beausang, Jane Cryan, Teresa Loftus, Patrick G Buckley, Michael Farrell, Seamus Looby, Richard Reilly, Francesca Brett, Hugh Kearney
INTRODUCTION: Machine learning methods have been introduced as a computer aided diagnostic tool, with applications to glioma characterisation on MRI. Such an algorithmic approach may provide a useful adjunct for a rapid and accurate diagnosis of a glioma. The aim of this study is to devise a machine learning algorithm that may be used by radiologists in routine practice to aid diagnosis of both: WHO grade and IDH mutation status in de novo gliomas. METHODS: To evaluate the status quo, we interrogated the accuracy of neuroradiology reports in relation to WHO grade: grade II 96...
May 16, 2018: Journal of Neuro-oncology
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/29761952/-automatic-classification-of-first-episode-drug-naive-schizophrenia-with-multi-modal-magnetic-resonance-imaging
#5
Yongzhe Yang, Yue Zhang, Fengchun Wu, Xiaobing Lu, Yuping Ning, Biao Huang, Xin Du, Chengwei Li, Kaixi Wang, Xiaoming Wu, Kai Wu
A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multi-modal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas...
October 1, 2017: Sheng Wu Yi Xue Gong Cheng Xue za Zhi, Journal of Biomedical Engineering, Shengwu Yixue Gongchengxue Zazhi
https://www.readbyqxmd.com/read/29761315/hair-detection-and-lesion-segmentation-in-dermoscopic-images-using-domain-knowledge
#6
Sameena Pathan, K Gopalakrishna Prabhu, P C Siddalingaswamy
Automated segmentation and dermoscopic hair detection are one of the significant challenges in computer-aided diagnosis (CAD) of melanocytic lesions. Additionally, due to the presence of artifacts and variation in skin texture and smooth lesion boundaries, the accuracy of such methods gets hampered. The objective of this research is to develop an automated hair detection and lesion segmentation algorithm using lesion-specific properties to improve the accuracy. The aforementioned objective is achieved in two ways...
May 15, 2018: Medical & Biological Engineering & Computing
https://www.readbyqxmd.com/read/29757718/us-of-right-upper-quadrant-pain-in-the-emergency-department-diagnosing-beyond-gallbladder-and-biliary-disease
#7
Gayatri Joshi, Kevin A Crawford, Tarek N Hanna, Keith D Herr, Nirvikar Dahiya, Christine O Menias
Acute cholecystitis is the most common diagnosable cause for right upper quadrant abdominal (RUQ) pain in patients who present to the emergency department (ED). However, over one-third of patients initially thought to have acute cholecystitis actually have RUQ pain attributable to other causes. Ultrasonography (US) is the primary imaging modality of choice for initial imaging assessment and serves as a fast, cost-effective, and dynamic modality to provide a definitive diagnosis or a considerably narrowed list of differential possibilities...
May 2018: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/29757717/radiographic-and-ct-features-of-viral-pneumonia
#8
Hyun Jung Koo, Soyeoun Lim, Jooae Choe, Sang-Ho Choi, Heungsup Sung, Kyung-Hyun Do
Viruses are the most common causes of respiratory infection. The imaging findings of viral pneumonia are diverse and overlap with those of other nonviral infectious and inflammatory conditions. However, identification of the underlying viral pathogens may not always be easy. There are a number of indicators for identifying viral pathogens on the basis of imaging patterns, which are associated with the pathogenesis of viral infections. Viruses in the same viral family share a similar pathogenesis of pneumonia, and the imaging patterns have distinguishable characteristics...
May 2018: Radiographics: a Review Publication of the Radiological Society of North America, Inc
https://www.readbyqxmd.com/read/29756129/deep-multi-task-multi-channel-learning-for-joint-classification-and-regression-of-brain-status
#9
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/29754995/additive-benefit-of-radiomics-over-size-alone-in-the-distinction-between-benign-lesions-and-luminal-a-cancers-on-a-large-clinical-breast-mri-dataset
#10
Heather M Whitney, Nathan S Taylor, Karen Drukker, Alexandra V Edwards, John Papaioannou, David Schacht, Maryellen L Giger
RATIONALE AND OBJECTIVES: The objective of this study was to demonstrate improvement in distinguishing between benign lesions and luminal A breast cancers in a large clinical breast magnetic resonance imaging database by using quantitative radiomics over maximum linear size alone. MATERIALS AND METHODS: In this retrospective study, 212 benign lesions and 296 luminal A breast cancers were automatically segmented from dynamic contrast-enhanced breast magnetic resonance images...
May 10, 2018: Academic Radiology
https://www.readbyqxmd.com/read/29750429/computer-aided-diagnosis-with-radiogenomics-analysis-of-the-relationship-between-genotype-and-morphological-changes-of-the-brain-magnetic-resonance-images
#11
Chiharu Kai, Yoshikazu Uchiyama, Junji Shiraishi, Hiroshi Fujita, Kunio Doi
In the post-genome era, a novel research field, 'radiomics' has been developed to offer a new viewpoint for the use of genotypes in radiology and medicine research which have traditionally focused on the analysis of imaging phenotypes. The present study analyzed brain morphological changes related to the individual's genotype. Our data consisted of magnetic resonance (MR) images of patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD), as well as their apolipoprotein E (APOE) genotypes...
May 10, 2018: Radiological Physics and Technology
https://www.readbyqxmd.com/read/29749141/particle-swarm-optimization-method-for-small-retinal-vessels-detection-on-multiresolution-fundus-images
#12
Bilal Khomri, Argyrios Christodoulidis, Leila Djerou, Mohamed Chaouki Babahenini, Farida Cheriet
Retinal vessel segmentation plays an important role in the diagnosis of eye diseases and is considered as one of the most challenging tasks in computer-aided diagnosis (CAD) systems. The main goal of this study was to propose a method for blood-vessel segmentation that could deal with the problem of detecting vessels of varying diameters in high- and low-resolution fundus images. We proposed to use the particle swarm optimization (PSO) algorithm to improve the multiscale line detection (MSLD) method. The PSO algorithm was applied to find the best arrangement of scales in the MSLD method and to handle the problem of multiscale response recombination...
May 2018: Journal of Biomedical Optics
https://www.readbyqxmd.com/read/29748869/classification-of-breast-masses-using-a-computer-aided-diagnosis-scheme-of-contrast-enhanced-digital-mammograms
#13
Gopichandh Danala, Bhavika Patel, Faranak Aghaei, Morteza Heidari, Jing Li, Teresa Wu, Bin Zheng
Contrast-enhanced digital mammography (CEDM) is a promising imaging modality in breast cancer diagnosis. This study aims to investigate how to optimally develop a computer-aided diagnosis (CAD) scheme of CEDM images to classify breast masses. A CEDM dataset of 111 patients was assembled, which includes 33 benign and 78 malignant cases. Each CEDM includes two types of images namely, low energy (LE) and dual-energy subtracted (DES) images. A CAD scheme was applied to segment mass regions depicting on LE and DES images separately...
May 10, 2018: Annals of Biomedical Engineering
https://www.readbyqxmd.com/read/29748039/comparing-panoramic-radiographs-and-cone-beam-computed-tomography-impact-on-radiographic-features-and-differential-diagnoses
#14
Li Zhen Lim, Ricardo J Padilla, Glenn J Reside, Donald A Tyndall
OBJECTIVES: The aims of this study were to determine whether lesion features appear differently on panoramic radiography (PAN) and cone beam computed tomography (CBCT), and whether the use of CBCT affects diagnostic accuracy and observers' confidence in comparison with PAN. STUDY DESIGN: Three oral and maxillofacial radiologists reviewed 33 sets of PAN images and CBCT images of biopsy-proven lesions. They described 12 different lesion features and provided up to 3 ranked differential diagnoses, as well as their confidence with respect to those diagnoses...
April 17, 2018: Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
https://www.readbyqxmd.com/read/29745550/-computer-aided-diagnosis-model-for-lung-tumor-based-on-ensemble-convolutional-neural-network
#15
Yuanyuan Wang, Tao Zhou, Huiling Lu, Cuiying Wu, Pengfei Yang
The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time...
August 1, 2017: Sheng Wu Yi Xue Gong Cheng Xue za Zhi, Journal of Biomedical Engineering, Shengwu Yixue Gongchengxue Zazhi
https://www.readbyqxmd.com/read/29745528/-a-novel-method-of-optic-disk-segmentation-based-on-visual-saliency-and-rotary-scanning
#16
Xinrong Cao, Lanyan Xue, Jiawen Lin, Lun Yu
Fast optic disk localization and boundary segmentation is an important research topic in computer aided diagnosis. This paper proposes a novel method to effectively segment optic disk by using human visual characteristics in analyzing and processing fundus image. After a general analysis of optic disk features in fundus images, the target of interest could be located quickly, and intensity, color and spatial distribution of the disc are used to generate saliency map based on pixel distance. Then the adaptive threshold is used to segment optic disk...
April 1, 2018: Sheng Wu Yi Xue Gong Cheng Xue za Zhi, Journal of Biomedical Engineering, Shengwu Yixue Gongchengxue Zazhi
https://www.readbyqxmd.com/read/29740749/overview-on-subjective-similarity-of-images-for-content-based-medical-image-retrieval
#17
REVIEW
Chisako Muramatsu
Computer-aided diagnosis systems for assisting the classification of various diseases have the potential to improve radiologists' diagnostic accuracy and efficiency, as reported in several studies. Conventional systems generally provide the probabilities of disease types in terms of numerical values, a method that may not be efficient for radiologists who are trained by reading a large number of images. Presentation of reference images similar to those of a new case being diagnosed can supplement the probability outputs based on computerized analysis as an intuitive guide, and it can assist radiologists in their diagnosis, reporting, and treatment planning...
May 8, 2018: Radiological Physics and Technology
https://www.readbyqxmd.com/read/29740722/computer-aided-diagnosis-for-123-i-fp-cit-imaging-impact-on-clinical-reporting
#18
Jonathan Christopher Taylor, Charles Romanowski, Eleanor Lorenz, Christine Lo, Oliver Bandmann, John Fenner
BACKGROUND: For (123 I)FP-CIT imaging, a number of algorithms have shown high performance in distinguishing normal patient images from those with disease, but none have yet been tested as part of reporting workflows. This study aims to evaluate the impact on reporters' performance of a computer-aided diagnosis (CADx) tool developed from established machine learning technology. Three experienced (123 I)FP-CIT reporters (two radiologists and one clinical scientist) were asked to visually score 155 reconstructed clinical and research images on a 5-point diagnostic confidence scale (read 1)...
May 8, 2018: EJNMMI Research
https://www.readbyqxmd.com/read/29739614/automated-classification-of-celiac-disease-during-upper-endoscopy-status-quo-and-quo-vadis
#19
M Gadermayr, G Wimmer, H Kogler, A Vécsei, D Merhof, A Uhl
A large amount of digital image material is routinely captured during esophagogastroduodenoscopies but, for the most part, is not used for confirming the diagnosis process of celiac disease which is primarily based on histological examination of biopsies. Recently, considerable effort has been undertaken to make use of image material by developing semi- or fully-automated systems to improve the diagnostic workup. Recently, focus was especially laid on developing state-of-the-art deep learning architectures, exploiting the endoscopist's expert knowledge and on making systems fully automated and thereby completely observer independent...
April 27, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/29737040/marked-accumulation-of-fdg-and-inflammatory-cells-expressing-glucose-transporter-3-in-igg4-related-autoimmune-hepatitis
#20
Toshihiro Araki, Teruko Arinaga-Hino, Hironori Koga, Jun Akiba, Tatsuya Ide, Yoshinobu Okabe, Reiichiro Kuwahara, Keisuke Amano, Makiko Yasumoto, Toshihiro Kawaguchi, Tomoya Sano, Reiichiro Kondou, Seiji Kurata, Keiichi Mitsuyama, Takuji Torimura
Immunoglobulin G (IgG) 4 related-autoimmune hepatitis (AIH) is a recently proposed subtype that responds well to steroid treatment; however, its pathogenesis remains unclear. We report here a 65-year-old Japanese female with skin itching and lip swelling. She had liver injury with jaundice, which persisted despite stopping of anti-allergic agents. Blood chemistry revealed highly elevated serum IgG and IgG4 (535 mg/dL) levels, and positive anti-nuclear antibody. The diagnosis of AIH was based on liver biopsy...
May 7, 2018: Hepatology Research: the Official Journal of the Japan Society of Hepatology
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