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SPIE medical Imaging 2017

作者： Sun, Min Chen, Xinjian Zhang, Zhiqiang Ma, Chiyuan Medical Image Processing Analysis and Visualization Lab School of Electronics and Information Engineering Soochow University Suzhou Jiangsu China

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A Framework of Whole Heart Extracellular Volume Fraction Estimation for Low-Dose Cardiac CT images

A Framework of Whole Heart Extracellular Volume Fraction Est...

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2015第七届国际心脏病大会

作者： Xinjian Chen Medical Image Processing Analysis & Visualization Lab School of Electrical and Information Engineering Soochow University

Cardiac CT(CCT) is widely available and has been validated for the detection of focal myocardial scar using a delayed enhancement technique in this ***,however,has not been previously evaluated for quantification of diffuse myocardial *** our investigation,we sought to evaluate the potential of low-dose CCT for the measurement of myocardial whole heart extracellular volume(ECV) *** is altered under conditions of increased myocardial fibrosis.A framework consisting of three main steps was proposed for CCT whole heart ECV ***,a shape-constrained graph cut(GC) method was proposed for myocardium and blood pool segmentation on postcontrast ***,the symmetric demons deformable registration method was applied to register precontrast to postcontrast *** the correspondences between the voxels from precontrast to postcontrast images were ***,the whole heart ECV value was *** proposed method was tested on 20 clinical low-dose CCT datasets with precontrast and postcontrast *** preliminary results demonstrated the feasibility and efficiency of the proposed method.

关键词： A Framework of Whole Heart Extracellular Volume Fraction Estimation for Low-Dose Cardiac CT images CCT Framework

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PDA-based system with teleradiology and image analysis capabilities

PDA-based system with teleradiology and image analysis capab...

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29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS 2007), vol.8

作者： Pantelis Georgiadis Dionisis Cavouras Antonis Daskalakis Koralia Sifaki Menelaos Malamas George Nikiforidis Ekaterini Solomon Medical Image Processing and Analysis Group (M.I.P.A.) Laboratory of Medical Physics School of Medicine University of Patras Greece Medical Signal and Image Processing Lab Department of Medical Instruments Technology Technological Education Institute of Athens Athens Greece MRI Unit General Hellenic Airforce Hospital Athens Greece Department of Radiology School of Medicine University of Patras Rio-Patras Greece

The aim of the present study was to design and implement a Personal Digital Assistant (PDA)-based teleradiology system incorporating image processing and analysis facilities for use in emergency situations within a hospital environment The system comprised a DICOM-server, connected to an MRI unit, 3 wireless access points, and 3 PDAs (HP iPaq rx3715). PDA application software was developed in MS Embedded Visual C++ 4.0. Each PDA can receive, load, process and analyze hi-quality static MR images. image processing includes gray-scale manipulation and spatial filtering techniques while image analysis incorporates a probabilistic neural network (PNN) classifier, which was optimally designed employing a suitable combination of textural features and was evaluated using the leave-one-out method. The PNN is capable of discriminating between three major types of human brain tumors with accuracy of 86.66%. The developed application may be useful as a mobile medical tele-consultation tool.

关键词： image analysis Personal digital assistants image processing Hospitals Magnetic resonance imaging Application software Gray-scale Filtering Biological neural networks Humans

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Statistical Validation of Automatic Methods for Hippocampus Segmentation in MR images of Epileptic Patients

Statistical Validation of Automatic Methods for Hippocampus ...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Dario Pompili Hamid Soltanian-Zadeh Department of Electrical and Computer Engineering Rutgers University Medical Image Analysis Lab Radiology Department Henry Ford Health System Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Hippocampus segmentation is a key step in the evaluation of mesial Temporal Lobe Epilepsy (mTLE) by MR images. Several automated segmentation methods have been introduced for medical image segmentation. Because of multiple edges, missing boundaries, and shape changing along its longitudinal axis, manual outlining still remains the bench-mark for hippocampus segmentation, which however, is impractical for large datasets due to time constraints. In this study, four automatic methods, namely FreeSurfer, Hammer, Automatic Brain Structure Segmentation (ABSS), and LocalInfo segmentation, are evaluated to find the most accurate and applicable method that resembles the bench-mark of hippocampus. Results from these four methods are compared against those obtained using manual segmentation for T1-weighted images of 157 symptomatic mTLE patients. For performance evaluation of automatic segmentation, Dice coefficient, Hausdorff distance, Precision, and Root Mean Square (RMS) distance are extracted and compared. Among these four automated methods, ABSS generates the most accurate results and the reproducibility is more similar to expert manual outlining by statistical validation. By considering p-value<0.05, the results of performance measurement for ABSS reveal that, Dice is 4%, 13%, and 17% higher, Hausdorff is 23%, 87%, and 70% lower, precision is 5%, -5%, and 12% higher, and RMS is 19%, 62%, and 65% lower compared to LocalInfo, FreeSurfer, and Hammer, respectively.

关键词： Segmentation Hippocampus Magnetic Resonance Imaging (MRI) Comparison Epilepsy

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A novel segmentation framework for uveal melanoma in magnetic resonance imaging based on class activation maps 2

A novel segmentation framework for uveal melanoma in magneti...

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2nd International Conference on medical Imaging with Deep Learning, MIDL 2019

作者： Nguyen, Huu-Giao Pica, Alessia Hrbacek, Jan Weber, Damien C. La Rosa, Francesco Schalenbourg, Ann Sznitman, Raphael Cuadra, Meritxell Bach Proton Therapy Center Paul Scherrer Institut ETH Domain Villigen Switzerland Ophthalmic Technology Lab. ARTORG Center University of Bern Switzerland Switzerland Radiation Oncology Department Inselspital University of Bern Switzerland Signal Processing Lab. Ecole Polytechnique Fédérale de Lausanne Switzerland Adult Ocular Oncology Unit Jules-Gonin Eye Hospital Lausanne Switzerland Medical Image Analysis Laboratory CIBM University of Lausanne Switzerland

An automatic and accurate eye tumor segmentation from Magnetic Resonance images (MRI) could have a great clinical contribution for the purpose of diagnosis and treatment planning of intra-ocula cancer. For instance, the characterization of uveal melanoma (UM) tumors would allow the integration of 3D information for the radiotherapy and would also support further radiomics studies. In this work, we tackle two major challenges of UM segmentation: 1) the high heterogeneity of tumor characterization in respect to location, size and appearance and, 2) the difficulty in obtaining ground-truth delineations of medical experts for training. We propose a thorough segmentation pipeline consisting of a combination of two Convolutional Neural Networks (CNN). First, we consider the class activation maps (CAM) output from a Resnet classification model and the combination of Dense Conditional Random Field (CRF) with a prior information of sclera and lens from an Active Shape Model (ASM) to automatically extract the tumor location for all MRIs. Then, these immediate results will be inputted into a 2D-Unet CNN whereby using four encoder and decoder layers to produce the tumor segmentation. A clinical data set of 1.5T T1-w and T2-w images of 28 healthy eyes and 24 UM patients is used for validation. We show experimentally in two different MRI sequences that our weakly 2D-Unet approach outperforms previous state-of-the-art methods for tumor segmentation and that it achieves equivalent accuracy as when manual labels are used for training. These results are promising for further large-scale analysis and for introducing 3D ocular tumor information in the therapy planning. © 2019 H.-G. Nguyen, A. Pica, J. Hrbacek, D. Weber, F.L. Rosa, A. Schalenbourg, R. Sznitman & M. Bach Cuadra.

关键词： Magnetic resonance imaging

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Support Vector Machine with Nonlinear-Kernel Optimization for Lateralization of Epileptogenic Hippocampus in MR images

Support Vector Machine with Nonlinear-Kernel Optimization fo...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Mohammad-Parsa Hosseini Mohammad R. Nazem-Zadeh Fariborz Mahmoudi Hao Ying Hamid Soltanian-Zadeh Medical Image Analysis Lab. Radiology Department Henry Ford Health System Department of Electrical and Computer Engineering Wayne State University Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran

ISBN: (纸本)9781424479276

Surgical treatment is suggested for seizure control in medically intractable epilepsy patients. Detailed pre-surgical evaluation and lateralization using Magnetic Resonance images (MRI) is expected to result in a successful surgical outcome. In this study, an optimized pattern recognition approach is proposed for lateralization of mesial Temporal Lobe Epilepsy (mTLE) patients using asymmetry of imaging indices of hippocampus. T1-weighted and Fluid-Attenuated Inversion Recovery (FLAIR) images of 76 symptomatic mTLE patients are considered. First, hippocampus is segmented using automatic and manual segmentation methods;then, volumetric and intensity features are extracted from the MR images. A nonlinear Support Vector Machine (SVM) with optimized Gaussian Radial Basis Function (GRBF) kernel is used to classify the imaging features. Using leave-one-out cross validation, this method results in a correct lateralization rate of 82%, a probability of detection for the left side of 0.90 (with false alarm probability of 0.04) and a probability of detection for the right side of 0.69 (with zero false alarm probability). The lateralization results are compared to linear SVM, multi-layer perceptron Artificial Neural Network (ANN), and volumetry and FLAIR asymmetry analysis. This lateralization method is suggested for pre-surgical evaluation using MRI before surgical treatment in mTLE patients. It achieves a more correct lateralization rate and fewer false positives.

关键词： Magnetic Resonance Imaging (MRI) Hippocampus Epilepsy Lateralization Support Vector Machine (SVM)

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Measures of the brain functional network that correlate with Alzheimer's neuropsychological test scores: An fMRI and graph analysis study

Measures of the brain functional network that correlate with...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Soroosh Golbabaei Amin Dadashi Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Radiology Medical Image Analysis Lab. Detroit Michigan USA

Neural degeneration in Alzheimer's disease (AD) leads to structural topology deformation that in turn changes brain functionality. The main aim of the present study is to find the brain's functional connectivity network (FCN) correlates of Alzheimer's psychological test scores. To this end, the brain's FCN is extracted from the resting state functional magnetic resonance images (rs-fMRI) of healthy controls and patients with AD and represented as a graph. Then, network measures are calculated from the graphs. The correlations between the brain network measures and five AD psychological assessment test scores are evaluated. The results show positive correlation between the Mini-Mental State Examination (MMSE) and nodal strength in the left insula and negative correlation between the Clinical Dementia Rating (CDR) score and local efficiency of left olfactory cortex and also eigenvector centrality of left supramarginal cortex. The Neuropsychiatric Inventory Questionnaire (NPI-Q) also seems to be correlated with network measures of the left superior parietal gyrus. Moreover, notable decreased continuity are spotted in the limbic system. These measures can be used to provide an objective tool for AD diagnosis.

关键词： Correlation Alzheimer's disease Psychology Olfactory Brain Atmospheric measurements

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Evaluating deformation patterns of the thoracic aorta in gated CTA sequences

Evaluating deformation patterns of the thoracic aorta in gat...

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IEEE International Symposium on Biomedical Imaging

作者： Ernst Schwartz Roman Gottardi Johannes Holfeld Christian Loewe Martin Czerny Georg Langs Computational Image Analysis and Radiology Lab (CIR) Medical University of Vienna Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Division of Cardiothoracic Surgery Department of Surgery Medical University of Vienna Austria Division of Interventional Radiology Department of Radiology Medical University of Vienna Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA

ISBN: (纸本)9781424441259;9781424441266

Cardiovascular interventions in the region of the aortic isthmus such as stent-grafting and vessel transposition introduce substantial changes in the deformation properties of the affected vessels. The changes play a fundamental role in the long-term prognosis for any such treatment, but are only poorly understood to date. We explore a fully automated method to quantify the deformation patterns of the thoracic aorta in gated computed tomography sequences. The aorta is segmented by a level set approach that accurately identifies the vessel lumen in each frame of the sequence. Consequently, landmarks on the vessel wall in each frame are registered using a probabilistic method. This allows for the measurement of global and local deformation properties. We evaluate our method on synthetic datasets and report first results of its application on real world data.

关键词： Biomedical imaging image segmentation Radiology Cardiology Surgery Computed tomography Level set Electrocardiography Pathology Deformable models

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Discovering true association between multimodal data sets using structured and sparse canonical correlation analysis: A simulation study

Discovering true association between multimodal data sets us...

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IEEE International Symposium on Biomedical Imaging

作者： Ali-Reza Mohammadi-Nejad Gholam-Ali Hossein-Zadeh Hamid Soltanian-Zadeh Control and Intelligent Processing Center of Excellence (CIPCE) University of Tehran Tehran Iran School of Cognitive Sciences Institute for Research in Fundamental Sciences (IPM) Tehran Iran Henry Ford Health System Medical Image Analysis Lab. Detroit Michigan USA

ISBN: (纸本)9781479923519

Recently, multi-modal data fusion emerged as a comprehensive approach in the field of neuroimaging. As a multivariate approach, canonical correlation analysis (CCA) is frequently used for data fusion. However, the current CCA-based fusion approaches face many problems in the sense of high-dimensionality, multi-colinearity, unimodal feature selection, asymmetric and reshaping of the imaging data into vectors. In this paper, we propose a structured and sparse CCA (ssCCA) technique to overcome the above problems. To investigate the performance of the proposed approach, we compared three multivariate and multi-modal fusion techniques: standard CCA, regularized sparse CCA, and ssCCA, with respect to their ability to detect multi-modal data associations. The results demonstrate that the ssCCA approach has superior recovery performance in the extraction of the true correlated variables relative to the other two fusion approaches.

关键词： Correlation Imaging Laplace equations Data integration Data models Standards Tuning

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A hierarchical image matting model for blood vessel segmentation in fundus images

arXiv

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arXiv 2017年

作者： Fan, Zhun Lu, Jiewei Li, Wenji Wei, Caimin Huang, Han Cai, Xinye Chen, Xinjian Guangdong Provincial Key Laboratory of Digital Signal and Image Processing College of Engineering Shantou University Shan'tou515063 China Department of Mathematics Shantou University Shan'tou515063 China School of Software Engineering South China University of Technology Guang'zhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiang'su210016 China Medical Image Processing and Analysis Lab School of Electronics and Information Engineering Soochow University Su'zhou215006 China

In this paper, a hierarchical image matting model is proposed to extract blood vessels from fundus images. More specifically, a hierarchical strategy utilizing the continuity and extendibility of retinal blood vessels is integrated into the image matting model for blood vessel segmentation. Normally the matting models require the user specified trimap, which separates the input image into three regions manually: the foreground, background and unknown regions. However, since creating a user specified trimap is a tedious and time-consuming task, region features of blood vessels are used to generate the trimap automatically in this paper. The proposed model has low computational complexity and outperforms many other state-of-art supervised and unsupervised methods in terms of accuracy, which achieves a vessel segmentation accuracy of 96.0%, 95.7% and 95.1% in an average time of 10.72s, 15.74s and 50.71s on images from three publicly available fundus image datasets DRIVE, STARE, and CHASE_DB1, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Blood vessels

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