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Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society

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https://www.readbyqxmd.com/read/29753964/a-web-based-system-for-neural-network-based-classification-in-temporomandibular-joint-osteoarthritis
#1
Priscille de Dumast, Clément Mirabel, Lucia Cevidanes, Antonio Ruellas, Marilia Yatabe, Marcos Ioshida, Nina Tubau Ribera, Loic Michoud, Liliane Gomes, Chao Huang, Hongtu Zhu, Luciana Muniz, Brandon Shoukri, Beatriz Paniagua, Martin Styner, Steve Pieper, Francois Budin, Jean-Baptiste Vimort, Laura Pascal, Juan Carlos Prieto
OBJECTIVE: The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). METHODS: This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans...
May 1, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29738914/g4dari-geant4-gate-based-monte-carlo-simulation-interface-for-dosimetry-calculation-in-radiotherapy
#2
Faiçal A A Slimani, Mahdjoub Hamdi, M'hamed Bentourkia
Monte Carlo (MC) simulation is widely recognized as an important technique to study the physics of particle interactions in nuclear medicine and radiation therapy. There are different codes dedicated to dosimetry applications and widely used today in research or in clinical application, such as MCNP, EGSnrc and Geant4. However, such codes made the physics easier but the programming remains a tedious task even for physicists familiar with computer programming. In this paper we report the development of a new interface GEANT4 Dose And Radiation Interactions (G4DARI) based on GEANT4 for absorbed dose calculation and for particle tracking in humans, small animals and complex phantoms...
May 1, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29775951/retinal-blood-vessel-segmentation-using-fully-convolutional-network-with-transfer-learning
#3
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/29753963/application-of-semi-automated-ultrasonography-on-nutritional-support-for-severe-acute-pancreatitis
#4
Ying Li, Yu Ye, Mei Yang, Haiying Ruan, Yuan Yu
OBJECTIVE: To evaluate the application value of semi-automated ultrasound on the guidance of nasogastrojejunal tube replacement for patients with acute severe pancreatitis (ASP), as well as the value of the nutritional support for standardized treatment in clinical practice. METHODS: The retrospective research was performed in our hospital, and 34 patients suffering from ASP were enrolled into this study. All these identified participants ever received CT scans in order to make definitive diagnoses...
April 25, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29684663/predictive-features-for-early-cancer-detection-in-barrett-s-esophagus-using-volumetric-laser-endomicroscopy
#5
Fons van der Sommen, Sander R Klomp, Anne-Fré Swager, Svitlana Zinger, Wouter L Curvers, Jacques J G H M Bergman, Erik J Schoon, Peter H N de With
The incidence of Barrett cancer is increasing rapidly and current screening protocols often miss the disease at an early, treatable stage. Volumetric Laser Endomicroscopy (VLE) is a promising new tool for finding this type of cancer early, capturing a full circumferential scan of Barrett's Esophagus (BE), up to 3-mm depth. However, the interpretation of these VLE scans can be complicated, due to the large amount of cross-sectional images and the subtle grayscale variations. Therefore, algorithms for automated analysis of VLE data can offer a valuable contribution to its overall interpretation...
April 13, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29702348/exploring-diagnosis-and-imaging-biomarkers-of-parkinson-s-disease-via-iterative-canonical-correlation-analysis-based-feature-selection
#6
Luyan Liu, Qian Wang, Ehsan Adeli, Lichi Zhang, Han Zhang, Dinggang Shen
Parkinson's disease (PD) is a neurodegenerative disorder that progressively hampers the brain functions and leads to various movement and non-motor symptoms. However, it is difficult to attain early-stage PD diagnosis based on the subjective judgment of physicians in clinical routines. Therefore, automatic and accurate diagnosis of PD is highly demanded, so that the corresponding treatment can be implemented more appropriately. In this paper, we focus on finding the most discriminative features from different brain regions in PD through T1-weighted MR images, which can help the subsequent PD diagnosis...
April 4, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29660595/computer-assisted-subtyping-and-prognosis-for-non-small-cell-lung-cancer-patients-with-unresectable-tumor
#7
Maliazurina Saad, Tae-Sun Choi
BACKGROUND: The histological classification or subtyping of non-small cell lung cancer is essential for systematic therapy decisions. Differentiating between the two main subtypes of pulmonary adenocarcinoma and squamous cell carcinoma highlights the considerable differences that exist in the prognosis of patient outcomes. Physicians rely on a pathological analysis to reveal these phenotypic variations that requires invasive methods, such as biopsy and resection sample, but almost 70% of tumors are unresectable at the point of diagnosis...
April 4, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29691123/a-computer-aided-detection-of-the-architectural-distortion-in-digital-mammograms-using-the-fractal-dimension-measurements-of-bemd
#8
Imad Zyout, Roberto Togneri
Achieving a high performance for the detection and characterization of architectural distortion in screening mammograms is important for an efficient breast cancer early detection. Viewing a mammogram image as a rough surface that can be described using the fractal theory is a well-recognized approach. This paper presents a new fractal-based computer-aided detection (CAD) algorithm for characterizing various breast tissues in screening mammograms with a particular focus on distinguishing between architectural distortion and normal breast parenchyma...
April 3, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29573582/deformable-respiratory-motion-correction-for-hepatic-rotational-angiography
#9
Alexandra Klugmann, Bastian Bier, Kerstin Müller, Andreas Maier, Mathias Unberath
Cone-beam rotational angiography enables 3D imaging of the hepatic vasculature and is considered beneficial for guidance of transcatheter arterial chemoembolization procedures. Respiratory motion during the rotational acquisition challenges state-of-the-art reconstruction algorithms as intra-scan motion leads to inconsistencies causing substantial blurring and streaking artifacts in uncompensated reconstructions, suggesting the need for motion correction. We propose an automated method for respiratory motion estimation and compensation based on registration of an initial 3D arterial model to vesselness enhanced 2D projection images...
March 17, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29573581/automatic-segmentation-of-pigment-deposits-in-retinal-fundus-images-of-retinitis-pigmentosa
#10
Nadia Brancati, Maria Frucci, Diego Gragnaniello, Daniel Riccio, Valentina Di Iorio, Luigi Di Perna
Retinitis Pigmentosa is an eye disease that presents with a slow loss of vision and then evolves until blindness results. The automatic detection of the early signs of retinitis pigmentosa acts as a great support to ophthalmologists in the diagnosis and monitoring of the disease in order to slow down the degenerative process. A large body of literature is devoted to the analysis of Retinitis Pigmentosa. However, all the existing approaches work on Optical Coherence Tomography (OCT) data, while hardly any attempts have been made working on fundus images...
March 17, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29573583/an-application-of-cascaded-3d-fully-convolutional-networks-for-medical-image-segmentation
#11
Holger R Roth, Hirohisa Oda, Xiangrong Zhou, Natsuki Shimizu, Ying Yang, Yuichiro Hayashi, Masahiro Oda, Michitaka Fujiwara, Kazunari Misawa, Kensaku Mori
Recent advances in 3D fully convolutional networks (FCN) have made it feasible to produce dense voxel-wise predictions of volumetric images. In this work, we show that a multi-class 3D FCN trained on manually labeled CT scans of several anatomical structures (ranging from the large organs to thin vessels) can achieve competitive segmentation results, while avoiding the need for handcrafting features or training class-specific models. To this end, we propose a two-stage, coarse-to-fine approach that will first use a 3D FCN to roughly define a candidate region, which will then be used as input to a second 3D FCN...
March 16, 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29609039/computer-aided-diagnosis-of-cavernous-malformations-in-brain-mr-images
#12
Huiquan Wang, S Nizam Ahmed, Mrinal Mandal
Cavernous malformation or cavernoma is one of the most common epileptogenic lesions. It is a type of brain vessel abnormality that can cause serious symptoms such as seizures, intracerebral hemorrhage, and various neurological disorders. Manual detection of cavernomas by physicians in a large set of brain MRI slices is a time-consuming and labor-intensive task and often delays diagnosis. In this paper, we propose a computer-aided diagnosis (CAD) system for cavernomas based on T2-weighted axial plane MRI image analysis...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29544118/automated-retinal-nerve-fiber-layer-defect-detection-using-fundus-imaging-in-glaucoma
#13
Rashmi Panda, N B Puhan, Aparna Rao, Debananda Padhy, Ganapati Panda
Retinal nerve fiber layer defect (RNFLD) provides an early objective evidence of structural changes in glaucoma. RNFLD detection is currently carried out using imaging modalities like OCT and GDx which are expensive for routine practice. In this regard, we propose a novel automatic method for RNFLD detection and angular width quantification using cost effective redfree fundus images to be practically useful for computer-assisted glaucoma risk assessment. After blood vessel inpainting and CLAHE based contrast enhancement, the initial boundary pixels are identified by local minima analysis of the 1-D intensity profiles on concentric circles...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29524784/psoriasis-image-representation-using-patch-based-dictionary-learning-for-erythema-severity-scoring
#14
Yasmeen George, Mohammad Aldeen, Rahil Garnavi
Psoriasis is a chronic skin disease which can be life-threatening. Accurate severity scoring helps dermatologists to decide on the treatment. In this paper, we present a semi-supervised computer-aided system for automatic erythema severity scoring in psoriasis images. Firstly, the unsupervised stage includes a novel image representation method. We construct a dictionary, which is then used in the sparse representation for local feature extraction. To acquire the final image representation vector, an aggregation method is exploited over the local features...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29523002/segmentation-of-white-matter-hyperintensities-using-convolutional-neural-networks-with-global-spatial-information-in-routine-clinical-brain-mri-with-none-or-mild-vascular-pathology
#15
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...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29510320/bone-fragment-segmentation-from-3d-ct-imagery
#16
Waseem G Shadid, Andrew Willis
This paper presents a novel method to segment bone fragments imaged using 3D Computed Tomography (CT). Existing image segmentation solutions often lack accuracy when segmenting internal trabecular and cancellous bone tissues from adjacent soft tissues having similar appearance and often merge regions associated with distinct fragments. These issues create problems in downstream visualization and pre-operative planning applications and impede the development of advanced image-based analysis methods such as virtual fracture reconstruction...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29481899/lumen-and-media-adventitia-border-detection-in-ivus-images-using-texture-enhanced-deformable-model
#17
Fang Chen, Ruibin Ma, Jia Liu, Mingyu Zhu, Hongen Liao
Lumen and media-adventitia (MA) borders in intravascular ultrasound (IVUS) images are critical for assessing the dimensions of vascular structures and providing plaque information in the diagnosis and navigation of vascular interventions. However, manual delineation of the lumen and MA borders is an intricate and time-consuming process. In this paper, a texture-enhanced deformable model (TEDM) is proposed to accurately detect these borders by incorporating texture information with the morphological factors of deformable model...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29426714/automated-analysis-and-classification-of-melanocytic-tumor-on-skin-whole-slide-images
#18
Hongming Xu, Cheng Lu, Richard Berendt, Naresh Jha, Mrinal Mandal
This paper presents a computer-aided technique for automated analysis and classification of melanocytic tumor on skin whole slide biopsy images. The proposed technique consists of four main modules. First, skin epidermis and dermis regions are segmented by a multi-resolution framework. Next, epidermis analysis is performed, where a set of epidermis features reflecting nuclear morphologies and spatial distributions is computed. In parallel with epidermis analysis, dermis analysis is also performed, where dermal cell nuclei are segmented and a set of textural and cytological features are computed...
June 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29579479/editorial-special-issue-on-advances-in-biomedical-image-processing
#19
EDITORIAL
Ewa Pietka, Arkadiusz Gertych
No abstract text is available yet for this article.
April 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29409716/efficient-deep-learning-model-for-mitosis-detection-using-breast-histopathology-images
#20
Monjoy Saha, Chandan Chakraborty, Daniel Racoceanu
Mitosis detection is one of the critical factors of cancer prognosis, carrying significant diagnostic information required for breast cancer grading. It provides vital clues to estimate the aggressiveness and the proliferation rate of the tumour. The manual mitosis quantification from whole slide images is a very labor-intensive and challenging task. The aim of this study is to propose a supervised model to detect mitosis signature from breast histopathology WSI images. The model has been designed using deep learning architecture with handcrafted features...
March 2018: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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