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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/29331208/wrist-a-wrist-image-segmentation-toolkit-for-carpal-bone-delineation-from-mri
#1
Brent Foster, Anand A Joshi, Marissa Borgese, Yasser Abdelhafez, Robert D Boutin, Abhijit J Chaudhari
Segmentation of the carpal bones from 3D imaging modalities, such as magnetic resonance imaging (MRI), is commonly performed for in vivo analysis of wrist morphology, kinematics, and biomechanics. This crucial task is typically carried out manually and is labor intensive, time consuming, subject to high inter- and intra-observer variability, and may result in topologically incorrect surfaces. We present a method, WRist Image Segmentation Toolkit (WRIST), for 3D semi-automated, rapid segmentation of the carpal bones of the wrist from MRI...
December 28, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29336922/segmentation-free-direct-tumor-volume-and-metabolic-activity-estimation-from-pet-scans
#2
Saeid Asgari Taghanaki, Noirin Duggan, Hillgan Ma, Xinchi Hou, Anna Celler, Francois Benard, Ghassan Hamarneh
Tumor volume and metabolic activity are two robust imaging biomarkers for predicting early therapy response in F-fluorodeoxyglucose (FDG) positron emission tomography (PET), which is a modality to image the distribution of radiotracers and thereby observe functional processes in the body. To date, estimation of these two biomarkers requires a lesion segmentation step. While the segmentation methods requiring extensive user interaction have obvious limitations in terms of time and reproducibility, automatically estimating activity from segmentation, which involves integrating intensity values over the volume is also suboptimal, since PET is an inherently noisy modality...
December 27, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29276002/intensity-based-volumetric-registration-of-magnetic-resonance-images-and-whole-mount-sections-of-the-prostate
#3
Are Losnegård, Lars Reisæter, Ole J Halvorsen, Christian Beisland, Aurea Castilho, Ludvig P Muren, Jarle Rørvik, Arvid Lundervold
OBJECTIVE: Magnetic Resonance Imaging (MRI) of the prostate provides useful in vivo diagnostic tissue information such as tumor location and aggressiveness, but ex vivo histopathology remains the ground truth. There are several challenges related to the registration of MRI to histopathology. We present a method for registration of standard clinical T2-weighted MRI (T2W-MRI) and transverse histopathology whole-mount (WM) sections of the prostate. METHODS: An isotropic volume stack was created from the WM sections using 2D rigid and deformable registration combined with linear interpolation...
December 15, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29127995/preface
#4
EDITORIAL
Daniel Racoceanu, Peter Hufnagl
No abstract text is available yet for this article.
November 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28676295/deep-convolutional-neural-networks-for-automatic-classification-of-gastric-carcinoma-using-whole-slide-images-in-digital-histopathology
#5
Harshita Sharma, Norman Zerbe, Iris Klempert, Olaf Hellwich, Peter Hufnagl
Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue...
November 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28648530/automatic-quantification-of-ihc-stain-in-breast-tma-using-colour-analysis
#6
M Milagro Fernández-Carrobles, Gloria Bueno, Marcial García-Rojo, Lucía González-López, Carlos López, Oscar Déniz
Immunohistochemical (IHC) biomarkers in breast tissue microarray (TMA) samples are used daily in pathology departments. In recent years, automatic methods to evaluate positive staining have been investigated since they may save time and reduce errors in the diagnosis. These errors are mostly due to subjective evaluation. The aim of this work is to develop a density tool able to automatically quantify the positive brown IHC stain in breast TMA for different biomarkers. To avoid the problem of colour variation and make a robust tool independent of the staining process, several colour standardization methods have been analysed...
November 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28499621/an-adaptive-positivity-thresholding-method-for-automated-ki67-hotspot-detection-akhod-in-breast-cancer-biopsies
#7
David Pilutti, Vincenzo Della Mea, Enrico Pegolo, Francesco La Marra, Fulvio Antoniazzi, Carla Di Loreto
The proliferative activity of breast cancer tissue can be estimated using the Ki67 biomarker. The percentage of positivity of such biomarker is correlated with proliferation and consequently with the prognosis of a breast tumor. Ki67 marked tissue samples are analyzed by an experienced pathologist who identifies the most active areas of tumor cell proliferation called hotspots, and estimates the positivity of each case. A method for the Automated Ki67 Hotspot Detection (AKHoD) is presented in this work. The main objective of the AKHoD method is to automatically and efficiently provide the pathologist with suggestions about Ki67 hotspot areas as a decision support...
November 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29137838/fusion-of-fmri-and-non-imaging-data-for-adhd-classification
#8
Atif Riaz, Muhammad Asad, Eduardo Alonso, Greg Slabaugh
Resting state fMRI has emerged as a popular neuroimaging method for automated recognition and classification of different brain disorders. Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common brain disorders affecting young children, yet its underlying mechanism is not completely understood and its diagnosis is mainly dependent on behavior analysis. This paper addresses the problem of classification of ADHD based on resting state fMRI and proposes a machine learning framework with integration of non-imaging data with imaging data to investigate functional connectivity alterations between ADHD and control subjects (not diagnosed with ADHD)...
October 19, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/29241972/towards-machine-learned-quality-control-a-benchmark-for-sharpness-quantification-in-digital-pathology
#9
Gabriele Campanella, Arjun R Rajanna, Lorraine Corsale, Peter J Schüffler, Yukako Yagi, Thomas J Fuchs
Pathology is on the verge of a profound change from an analog and qualitative to a digital and quantitative discipline. This change is mostly driven by the high-throughput scanning of microscope slides in modern pathology departments, reaching tens of thousands of digital slides per month. The resulting vast digital archives form the basis of clinical use in digital pathology and allow large scale machine learning in computational pathology. One of the most crucial bottlenecks of high-throughput scanning is quality control (QC)...
September 25, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28701281/editorial-for-the-special-issue-of-computational-methods-for-molecular-imaging-for-computerized-medical-imaging-and-graphics
#10
EDITORIAL
Kuangyu Shi, Fei Gao, Nassir Navab, Julia Schnabel, Pengcheng Shi, Sibylle I Ziegler
No abstract text is available yet for this article.
September 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28669577/a-hierarchical-convolutional-neural-network-for-vesicle-fusion-event-classification
#11
Haohan Li, Yunxiang Mao, Zhaozheng Yin, Yingke Xu
Quantitative analysis of vesicle exocytosis and classification of different modes of vesicle fusion from the fluorescence microscopy are of primary importance for biomedical researches. In this paper, we propose a novel Hierarchical Convolutional Neural Network (HCNN) method to automatically identify vesicle fusion events in time-lapse Total Internal Reflection Fluorescence Microscopy (TIRFM) image sequences. Firstly, a detection and tracking method is developed to extract image patch sequences containing potential fusion events...
September 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28223022/early-identification-of-mild-cognitive-impairment-using-incomplete-random-forest-robust-support-vector-machine-and-fdg-pet-imaging
#12
Shen Lu, Yong Xia, Weidong Cai, Michael Fulham, David Dagan Feng
Alzheimer's disease (AD) is the most common type of dementia and will be an increasing health problem in society as the population ages. Mild cognitive impairment (MCI) is considered to be a prodromal stage of AD. The ability to identify subjects with MCI will be increasingly important as disease modifying therapies for AD are developed. We propose a semi-supervised learning method based on robust optimization for the identification of MCI from [18F]Fluorodeoxyglucose PET scans. We extracted three groups of spatial features from the cortical and subcortical regions of each FDG-PET image volume...
September 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28087102/feature-selection-for-outcome-prediction-in-oesophageal-cancer-using-genetic-algorithm-and-random-forest-classifier
#13
Desbordes Paul, Ruan Su, Modzelewski Romain, Vauclin Sébastien, Vera Pierre, Gardin Isabelle
The outcome prediction of patients can greatly help to personalize cancer treatment. A large amount of quantitative features (clinical exams, imaging, …) are potentially useful to assess the patient outcome. The challenge is to choose the most predictive subset of features. In this paper, we propose a new feature selection strategy called GARF (genetic algorithm based on random forest) extracted from positron emission tomography (PET) images and clinical data. The most relevant features, predictive of the therapeutic response or which are prognoses of the patient survival 3 years after the end of treatment, were selected using GARF on a cohort of 65 patients with a local advanced oesophageal cancer eligible for chemo-radiation therapy...
September 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/27955798/automatic-detection-and-classification-of-regions-of-fdg-uptake-in-whole-body-pet-ct-lymphoma-studies
#14
Lei Bi, Jinman Kim, Ashnil Kumar, Lingfeng Wen, Dagan Feng, Michael Fulham
[(18)F]-Fluorodeoxyglucose (FDG) positron emission tomography-computed tomography (PET-CT) scans of lymphoma patients usually show disease involvement as foci of increased radiotracer uptake. Existing methods for detecting abnormalities, model the characteristics of these foci; this is challenging due to the inconsistent shape and localization information about the lesions. Thresholding the degree of FDG uptake is the standard method to separate different sites of involvement. But may fragment sites into smaller regions, and may also incorrectly identify sites of normal physiological FDG uptake and normal FDG excretion (sFEPU) such as the kidneys, bladder, brain and heart...
September 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/27914956/individual-refinement-of-attenuation-correction-maps-for-hybrid-pet-mr-based-on-multi-resolution-regional-learning
#15
Kuangyu Shi, Sebastian Fürst, Liang Sun, Mathias Lukas, Nassir Navab, Stefan Förster, Sibylle I Ziegler
PET/MR is an emerging hybrid imaging modality. However, attenuation correction (AC) remains challenging for hybrid PET/MR in generating accurate PET images. Segmentation-based methods on special MR sequences are most widely recommended by vendors. However, their accuracy is usually not high. Individual refinement of available certified attenuation maps may be helpful for further clinical applications. In this study, we proposed a multi-resolution regional learning (MRRL) scheme to utilize the internal consistency of the patient data...
September 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/27887989/quantitative-pet-image-reconstruction-employing-nested-expectation-maximization-deconvolution-for-motion-compensation
#16
Nicolas A Karakatsanis, Charalampos Tsoumpas, Habib Zaidi
Bulk body motion may randomly occur during PET acquisitions introducing blurring, attenuation-emission mismatches and, in dynamic PET, discontinuities in the measured time activity curves between consecutive frames. Meanwhile, dynamic PET scans are longer, thus increasing the probability of bulk motion. In this study, we propose a streamlined 3D PET motion-compensated image reconstruction (3D-MCIR) framework, capable of robustly deconvolving intra-frame motion from a static or dynamic 3D sinogram. The presented 3D-MCIR methods need not partition the data into multiple gates, such as 4D MCIR algorithms, or access list-mode (LM) data, such as LM MCIR methods, both associated with increased computation or memory resources...
September 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28927549/improved-threshold-selection-for-the-determination-of-volume-of-distribution-of-nanoparticles-administered-by-convection-enhanced-delivery
#17
David Lei Chi, Eric Song, Alice Gaudin, W Mark Saltzman
Nanotechnology, in conjunction with convection-enhanced delivery (CED), has gained traction as a promising method to treat many debilitating neurological diseases, including gliomas. One of the key parameters to evaluate the effectiveness of delivery is the volume of distribution (Vd) of nanoparticles within the brain parenchyma. Measurements of Vd are commonly made using fluorescent reporter systems. However, reported analyses lack accurate and robust methods for determining Vd. Current methods face the problems of varying background intensities between images, high intensity aggregates that can shift intensity distributions, and faint residual backgrounds that can occur as artifacts of fluorescent imaging...
August 24, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28886885/segmentation-of-the-right-ventricle-in-four-chamber-cine-cardiac-mr-images-using-polar-dynamic-programming
#18
Jose A Rosado-Toro, Aiden Abidov, Maria I Altbach, Isabel B Oliva, Jeffrey J Rodriguez, Ryan J Avery
The four chamber plane is currently underutilized in the right ventricular segmentation community. Four chamber information can be useful to determine ventricular short axis stacks and provide a rough estimate of the right ventricle in short axis stacks. In this study, we develop and test a semi-automated technique for segmenting the right ventricle in four chamber cine cardiac magnetic resonance images. The three techniques that use minimum cost path algorithms were used. The algorithms are: Dijkstra's shortest path algorithm (Dijkstra), an A* algorithm that uses length, curvature and torsion into an active contour model (ALCT), and a variation of polar dynamic programming (PDP)...
August 18, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28807362/leg-movement-tracking-in-automatic-video-based-one-leg-stance-evaluation
#19
Jacek Kawa, Paula Stępień, Wojciech Kapko, Aleksandra Niedziela, Jarosław Derejczyk
Falls are a major risk in elder population. Early diagnosis is therefore an important step towards increasing the safety of elders. One of the common diagnostic tests is the Berg Balance Scale (BBS), consisting of 14 exercises arranged from the easiest (sitting-to-standing) to the most demanding (one-leg stance). In this study a novel approach to the automatic assessment of the time in which the patient can remain in the one-leg stance position without loosing balance is introduced. The data is collected using a regular video camera...
August 3, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://www.readbyqxmd.com/read/28807363/deducing-magnetic-resonance-neuroimages-based-on-knowledge-from-samples
#20
Yuwei Jiang, Feng Liu, Mingxia Fan, Xuzhou Li, Zhiyong Zhao, Zhaoling Zeng, Yi Wang, Dongrong Xu
PURPOSE: Because individual variance always exists, using the same set of predetermined parameters for magnetic resonance imaging (MRI) may not be exactly suitable for each participant. We propose a knowledge-based method that can repair MRI data of undesired contrast as if a new scan were acquired using imaging parameters that had been individually optimized. METHODS: The method employed a strategy called analogical reasoning to deduce voxel-wise relaxation properties using morphological and biological similarity...
July 29, 2017: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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