DOCSLIB.ORG

Using Convolutional Neural Network to Generate Neuro Image Template

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Using Convolutional Neural Network to Generate Neuro Image Template Using Convolutional Neural Network to Generate Neuro Image Template A Thesis Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University By Songyue Qian, Graduate Program in Department of Electrical and Computer Engineering The Ohio State University 2019 Master's Examination Committee: Prof. Bradley D. Clymer, PhD, Co-Advisor Assistant Professor Barbaros Selnur Erdal, PhD, Co-Advisor c Copyright by Songyue Qian 2019 Abstract Machine learning techniques implemented by Convolutional Neural Network (CNN) have become the state-of-the-art technique for automatic neuro image classification because of their outstanding computing ability. The template model method, a tech- nique used to produce a stable pattern from the position of different anatomical shapes in the brain, is considered as another automatic method that can segment whether a patient is healthy or not. However, most machine learning techniques only focus on detecting specific diseases but overlook the patients health. The template model's architecture may overlook the pattern of the entire brain. Therefore, a bet- ter automatic classification technique that focuses on the patient's health status is in demand. The purpose of this research is to design an efficient CNN architecture sensitive on normal case diagnosis instead of disease detection. In this research, we propose Hybrid-CNN-Siamese-Network(HCSNet), a CNN ar- chitecture which is used for 2D neuro image classification. HCSNet combines Incep- tion ResNet V2 and a Siamese network together to achieve better performance than those two methods alone. In order to illustrate why this architecture performs well on neuro imaging, a comprehensive overview of existing techniques for CNN analysis and experiments about comparing different CNNs' performance on the same neuro image dataset are provided [1]. We merge all the abnormal cases to one abnormal ii class so that there are only two classes (normal/abnormal) in the dataset. Experimen- tal results demonstrate the effectiveness of our proposed CNN architecture. HCSNet obtains an overall 92% accuracy and 94.4% accuracy for detecting normal cases. We further discuss the potential future work that would be done based on this CNN architecture. iii This is dedicated to my parents. iv Acknowledgments I would like to thank my two thesis advisors Prof. Bradley D. Clymer of the Electrical and Computer Engineering at the Ohio State University and Dr. Barbaros Selnur Erdal of the Department of the Radiology at the Ohio State University. Dr. Clymer, thank you for the guiding and patience to me. Dr. Erdal, it's my honor to work with you, you are the best advisor. Thank you. I would also like to thank all of my friends who have helped me to improve the writing. It is impossible for me to finish this thesis without you guys' assistance and support.Thank you. Songyue Qian v Table of Contents Page Abstract . ii Dedication . iv Acknowledgments . v List of Tables . viii List of Figures . x 1. Introduction . 1 1.1 Motivation . 1 1.2 Problem Statement . 2 1.3 Organization . 3 2. Background . 5 2.1 Neuroimaging template . 5 2.2 Neural Network Architecture . 7 2.3 Contribution . 9 3. Methodology . 11 3.1 Overview of Convolutional Neural Network(CNN) . 11 3.2 Neural Network Training . 14 3.2.1 Backpropagation . 14 3.2.2 Training sets setting . 15 3.3 Neural Network Layers . 16 3.3.1 Convolutional Layers . 16 3.3.2 Pooling Layers . 21 vi 3.3.3 Activation Layers . 23 3.3.4 Dropout . 27 3.3.5 Normalization Layers . 27 3.3.6 Loss Layers . 29 3.4 Neural Network design . 30 3.4.1 LeNet . 30 3.4.2 AlexNet . 31 3.4.3 VGGNet . 34 3.4.4 GoogleNet . 37 3.4.5 ResNet . 41 3.4.6 Siamese Network . 45 3.4.7 Hybrid CNN-Siamese network(HCSNet) . 48 3.5 Pre-processing . 50 3.6 Analysis Techniques . 51 3.6.1 Qualitative Analysis by example . 51 3.6.2 Confusion Matrices . 53 3.6.3 Learning Curves . 55 3.6.4 Others . 55 4. Experiment and Result . 57 4.1 Parameter setting . 57 4.1.1 Implementation on scratch network . 58 4.1.2 Implementation on pre-trained network . 59 4.2 Result and Statistics on Experiment I . 60 4.3 Result and Statistics on Experiment II . 66 5. Conclusion and Future Discussion . 72 5.1 Conclusion . 72 5.2 Future work and discussion . 73 5.2.1 Datasets size . 73 5.2.2 Siamese network parameter setting . 73 5.2.3 Segmentation region . 73 5.2.4 3D object processing . 74 Bibliography . 75 vii List of Tables Table Page 3.1 Example of Confusion matrix . 54 4.1 DataSet distribution on Experiment I . 59 4.2 DataSet distribution on experiment II . 60 4.3 Result of LeNet after 5000 steps in set 1 . 62 4.4 Result of LeNet after 5000 steps in set 2 . 62 4.5 Result of AlexNet after 5000 steps in set 1 . 62 4.6 Result of AlexNet after 5000 steps in set 2 . 62 4.7 Result of VGG-16 after 5000 steps in set 1 . 63 4.8 Result of VGG-16 after 5000 steps in set 2 . 63 4.9 Result of Inception v3 after 5000 steps in set 1 . 63 4.10 Result of Inception v3 after 5000 steps in set 2 . 63 4.11 Result of Inception v4 after 5000 steps in set 1 . 64 4.12 Result of Inception v4 after 5000 steps in set 2 . 64 4.13 Result of Inception ResNet after 5000 steps in set 1 . 64 4.14 Result of Inception ResNet after 5000 steps in set 2 . 64 viii 4.15 Result of Siamese Network after 5000 steps in set 1 . 65 4.16 Result of Siamese Network after 5000 steps in set 2 . 65 4.17 Result of HCSNet after 5000 steps in set 1 . 65 4.18 Result of HCSNet after 5000 steps in set 2 . 65 4.19 Result of Inception V3 in Experiment II . 67 4.20 Result of Inception ResNet V2 in Experiment II . 68 4.21 Result of Inception V3 and Siamese Network in Experiment II . 69 4.22 Result of HCSNet in Experiment II . 70 ix List of Figures Figure Page 3.1 Multiple Neurons with single directions node . 11 3.2 Multiple Neurons with single directions node . 12 3.3 Multiple Neurons with single directions node . 13 3.4 Convolutional Process . 18 3.5 Convolutional Matrix . 18 3.6 Convolutional Matrix with padding=2 . 20 3.7 Max and Average pooling . 22 3.8 Sigmoid activation function . 24 3.9 Derivative of Sigmoid Activation Function . 24 3.10 tanh activation function . 25 3.11 Derivative of tanh Activation Function . 25 3.12 ReLU activation function . 25 3.13 Derivative of ReLU Activation Function . 25 3.14 Leaky ReLU Activation . 26 3.15 Before and after applying dropout . 28 x 3.16 Dropout processing equation[2] . 29 3.17 Architecture of LeNet-5[3] . 31 3.18 Layer structure of AlexNet[4] . 32 3.19 Parameter number and setting of AlexNet . 33 3.20 Architecture of VGGNet[5] . 35 3.21 Inception module of GoogleNet . 38 3.22 Inception V2 module . 40 3.23 Inception V3 module . 41 3.24 Residual unit of ResNet . 42 3.25 Compare VGG and ResNet structure . 44 3.26 Residual unit of two and three layer ResNet . 45 3.27 Siamese Architecture [6] . 46 3.28 Workflow of HCSNet . 49 4.1 Different network validation accuracy vs. number of steps in set 1 . 61 4.2 Different network validation accuracy vs. number of steps in set 2 . 61 4.3 Validation accuracy vs. epoch Inception V3 Experiment II . 67 4.4 Loss vs. epoch Inception V3 Experiment II . 68 4.5 Validation accuracy vs. epoch Inception ResNet V2 Experiment II . 69 4.6 Loss vs. epoch Inception ResNet V2 Experiment II . 70 xi Chapter 1: Introduction 1.1 Motivation While the usefulness of medical imaging is constantly leading to better image quality and higher accuracy of segmentation results, manual segmentation is a time- consuming and laborious process. Hence, automatic segmentation is a demanded technique which is mostly done by quantitative research algorithms. In general, most of the quantitative research about neuroimaging focuses on aligning one or several anatomical templates to the target image (via a linear or nonlinear registration pro- cess) and transferring segmentation labels from the templates to the image. However, such methods may not be capable of capturing the full anatomical variability of tar- gets because the generated model may only focus on the major components of the brain structure. One of the quantitative research algorithms generates a template.
Load more

Recommended publications
  • Computational Anatomy: Model Construction and Application for Anatomical Structures Recognition in Torso CT Images
    The First International Symposium on the Project “Computational Anatomy” Computational Anatomy: Model Construction and Application for Anatomical Structures Recognition in Torso CT Images Hiroshi Fujita #1, Takeshi Hara #2, Xiangrong Zhou #3, Huayue Chen *4, and Hiroaki Hoshi **5 # Department of Intelligent Image Information, Graduate School of Medicine, Gifu University * Department of Anatomy, Graduate School of Medicine, Gifu University ** Department of Radiology, Graduate School of Medicine, Gifu University Yanagido 1-1, Gifu 501-1194, Japan 1 [email protected] 2 [email protected] 3 [email protected] 4 [email protected] 5 [email protected] Abstract—This paper describes the purpose and recent summary advanced CAD system that aims at multi-disease detection of our research work which is a part of research project from multi-organ [2]. The traditional approach for lesion “Computational anatomy for computer-aided diagnosis and detection prepares a pattern for a specific lesion type and therapy: Frontiers of medical image sciences” that was funded searches the lesion candidates in CT images by a pattern by Grant-in-Aid for Scientific Research on Innovative Areas, matching process. In the case of multi-disease detection from MEXT, Japan. The main purpose of our research in this project focus on model construction of computational anatomy and multi-organ, generating and matching a lot of prior lesion model applications for analyzing and recognizing the anatomical patterns from CT images can be tedious and sometimes not structures of human torso region using high-resolution torso X- possible.
    [Show full text]
  • Using Deep Learning Convolutional Neural Network
    Yang et al. BioMed Eng OnLine (2018) 17:138 https://doi.org/10.1186/s12938-018-0587-0 BioMedical Engineering OnLine RESEARCH Open Access Multimodal MRI‑based classifcation of migraine: using deep learning convolutional neural network Hao Yang†, Junran Zhang*†, Qihong Liu and Yi Wang *Correspondence: [email protected] Abstract †Hao Yang and Junran Zhang Background: Recently, deep learning technologies have rapidly expanded into medi- contributed equally to this work cal image analysis, including both disease detection and classifcation. As far as we Department of Medical know, migraine is a disabling and common neurological disorder, typically character- Information Engineering, ized by unilateral, throbbing and pulsating headaches. Unfortunately, a large number School of Electrical Engineering and Information, of migraineurs do not receive the accurate diagnosis when using traditional diagnostic Sichuan University, Chengdu, criteria based on the guidelines of the International Headache Society. As such, there Sichuan, China is substantial interest in developing automated methods to assist in the diagnosis of migraine. Methods: To the best of our knowledge, no studies have evaluated the potential of deep learning technologies in assisting with the classifcation of migraine patients. Here, we used deep learning methods in combination with three functional measures (the amplitude of low-frequency fuctuations, regional homogeneity and regional functional correlation strength) based on rs-fMRI data to distinguish not only between migraineurs and healthy controls, but also between the two subtypes of migraine. We employed 21 migraine patients without aura, 15 migraineurs with aura, and 28 healthy controls. Results: Compared with the traditional support vector machine classifer, which has an accuracy of 83.67%, our Inception module-based convolutional neural network approach showed a signifcant improvement in classifcation output (over 86.18%).
    [Show full text]
  • A Review on Deep Learning in Medical Image Reconstruction ∗
    A Review on Deep Learning in Medical Image Reconstruction ∗ Haimiao Zhang† and Bin Dong†‡ June 26, 2019 Abstract. Medical imaging is crucial in modern clinics to provide guidance to the diagnosis and treatment of diseases. Medical image reconstruction is one of the most fundamental and important components of medical imaging, whose major objective is to acquire high-quality medical images for clinical usage at the minimal cost and risk to the patients. Mathematical models in medical image reconstruction or, more generally, image restoration in computer vision, have been playing a promi- nent role. Earlier mathematical models are mostly designed by human knowledge or hypothesis on the image to be reconstructed, and we shall call these models handcrafted models. Later, handcrafted plus data-driven modeling started to emerge which still mostly relies on human designs, while part of the model is learned from the observed data. More recently, as more data and computation resources are made available, deep learning based models (or deep models) pushed the data-driven modeling to the extreme where the models are mostly based on learning with minimal human designs. Both handcrafted and data-driven modeling have their own advantages and disadvantages. Typical hand- crafted models are well interpretable with solid theoretical supports on the robustness, recoverability, complexity, etc., whereas they may not be flexible and sophisticated enough to fully leverage large data sets. Data-driven models, especially deep models, on the other hand, are generally much more flexible and effective in extracting useful information from large data sets, while they are currently still in lack of theoretical foundations.
    [Show full text]
  • Deep Learning in Bioinformatics
    Deep Learning in Bioinformatics Seonwoo Min1, Byunghan Lee1, and Sungroh Yoon1,2* 1Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea 2Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 08826, Korea Abstract In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. *Corresponding author. Mailing address: 301-908, Department of Electrical and Computer Engineering, Seoul National University, Seoul 08826, Korea. E-mail: [email protected]. Phone: +82-2-880-1401. Keywords Deep learning, neural network, machine learning, bioinformatics, omics, biomedical imaging, biomedical signal processing Key Points As a great deal of biomedical data have been accumulated, various machine algorithms are now being widely applied in bioinformatics to extract knowledge from big data.
    [Show full text]
  • Medical Image Computing: Machine-Learning Methods and Advanced-MRI Applications
    Medical Image Computing: Machine-learning methods and Advanced-MRI applications Overview Medical imaging is increasingly being used as a first step for clinical diagnosis of a large number of diseases, including disorders of the brain, heart, lung, kidney, muscle, etc. Among the modalities that find widespread use is magnetic resonance imaging (MRI), due its ability to image bones as well as soft tissue. In the case of major abnormalities such as tumors, a radiologist can easily spot abnormalities in the MR images. However, subtle changes in the tissue are difficult to locate for the human eye. Hence, advanced image processing techniques can be of great medical value to assist the radiologist as well as to understand the basic mechanism of action in a large number of diseases. Further, early diagnosis is critical for better clinical outcome. This course is designed as an introductory course in medical image processing and analysis for engineers and quantitative scientists, including students and faculty. It will primarily focus on MR imaging and analysis with a special emphasis on brain and mental disorders. Apart from the basics such as edge detection and segmentation, we will also cover advanced concepts such as shape analysis, probabilistic clustering, nonlinear dimensionality reduction, and diffusion MRI processing. This course will also have several hands-on segments that will allow the students to get a first-hand feel of the opportunities and challenges in medical image analysis. Course participants will learn these topics through lectures and hands-on experiments. Also, case studies and assignments will be shared to stimulate research motivation of participants.
    [Show full text]
  • MR Images, Brain Lesions, and Deep Learning
    applied sciences Review MR Images, Brain Lesions, and Deep Learning Darwin Castillo 1,2,3,* , Vasudevan Lakshminarayanan 2,4 and María José Rodríguez-Álvarez 3 1 Departamento de Química y Ciencias Exactas, Sección Fisicoquímica y Matemáticas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n, Loja 11-01-608, Ecuador 2 Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L3G1, Canada; [email protected] 3 Instituto de Instrumentación para Imagen Molecular (i3M) Universitat Politècnica de València—Consejo Superior de Investigaciones Científicas (CSIC), E-46022 Valencia, Spain; [email protected] 4 Departments of Physics, Electrical and Computer Engineering and Systems Design Engineering, University of Waterloo, Waterloo, ON N2L3G1, Canada * Correspondence: [email protected]; Tel.: +593-07-370-1444 (ext. 3204) Featured Application: This review provides a critical review of deep/machine learning algo- rithms used in the identification of ischemic stroke and demyelinating brain diseases. It eval- uates their strengths and weaknesses when applied to real world clinical data. Abstract: Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study.
    [Show full text]
  • Medical Image Processing: from Formation to Interpretation
    Technical Article Share on Twitter Facebook LinkedIn Email Medical Image Processing: From Formation to Interpretation Anton Patyuchenko Analog Devices, Inc. Technological advancements achieved in medical imaging over the last Core Areas of Medical Image Processing century created unprecedented opportunities for noninvasive diagnostic There are numerous concepts and approaches for structuring the field of and established medical imaging as an integral part of healthcare systems medical image processing that focus on different aspects of its core areas today. One of the major areas of innovation representing these advance- illustrated in Figure 1. These areas shape three major processes underlying ments is the interdisciplinary field of medical image processing. this field—image formation, image computing, and image management. This field of rapid development deals with a broad number of processes The process of image formation is comprised of data acquisition and ranging from raw data acquisition to digital image communication that image reconstruction steps, providing a solution to a mathematical inverse underpin the complete data flow in modern medical imaging systems. problem. The purpose of image computing is to improve interpretability of Nowadays, these systems offer increasingly higher resolutions in spatial the reconstructed image and extract clinically relevant informa tion from it. and intensity dimensions, as well as faster acquisition times resulting in an Finally, image management deals with compression, archiving, retrieval, extensive amount of high quality raw image data that must be properly and communication of the acquired images and derived information. processed and interpreted to attain accurate diagnostic results. This article focuses on the key areas of medical image processing, consid- ers the context of specific imaging modalities, and discusses the key challenges and trends in this field.
    [Show full text]
  • Computational Anatomy for Multi-Organ Analysis in Medical Imaging: a Review
    Medical Image Analysis 56 (2019) 44–67 Contents lists available at ScienceDirect Medical Image Analysis journal homepage: www.elsevier.com/locate/media Computational anatomy for multi-organ analysis in medical imaging: A review ∗ Juan J. Cerrolaza a, , Mirella López Picazo b,c, Ludovic Humbert c, Yoshinobu Sato d, Daniel Rueckert a, Miguel Ángel González Ballester b,e, Marius George Linguraru f,g a Biomedical Image Analysis Group, Imperial College London, United Kingdom b BCN Medtech, Dept. of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain c Galgo Medical S.L., Spain d Graduate School of Information Science, Nara Institute of Science and Technology (NAIST), Nara, Japan e ICREA, Barcelona, Spain f Sheickh Zayed Institute for Pediatric Surgicaonl Innovation, Children’s National Health System, Washington DC, USA g School of Medicine and Health Sciences, George Washington University, Washington DC, USA a r t i c l e i n f o a b s t r a c t Article history: The medical image analysis field has traditionally been focused on the development of organ-, and Received 20 August 2018 disease-specific methods. Recently, the interest in the development of more comprehensive computa- Revised 5 February 2019 tional anatomical models has grown, leading to the creation of multi-organ models. Multi-organ ap- Accepted 13 April 2019 proaches, unlike traditional organ-specific strategies, incorporate inter-organ relations into the model, Available online 15 May 2019 thus leading to a more accurate representation of the complex human anatomy. Inter-organ relations Keywords: are not only spatial, but also functional and physiological.
    [Show full text]
  • Introduction to Medical Image Computing
    1 MEDICAL IMAGE COMPUTING (CAP 5937)- SPRING 2017 LECTURE 1: Introduction Dr. Ulas Bagci HEC 221, Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), Orlando, FL 32814. [email protected] or [email protected] 2 • This is a special topics course, offered for the second time in UCF. Lorem Ipsum Dolor Sit Amet CAP5937: Medical Image Computing 3 • This is a special topics course, offered for the second time in UCF. • Lectures: Mon/Wed, 10.30am- 11.45am Lorem Ipsum Dolor Sit Amet CAP5937: Medical Image Computing 4 • This is a special topics course, offered for the second time in UCF. • Lectures: Mon/Wed, 10.30am- 11.45am • Office hours: Lorem Ipsum Dolor Sit Amet Mon/Wed, 1pm- 2.30pm CAP5937: Medical Image Computing 5 • This is a special topics course, offered for the second time in UCF. • Lectures: Mon/Wed, 10.30am-11.45am • Office hours: Mon/Wed, 1pm- 2.30pm • No textbook is Lorem Ipsum Dolor Sit Amet required, materials will be provided. • Avg. grade was A- last CAP5937: Medical Image Computing spring. 6 Image Processing Computer Vision Medical Image Imaging Computing Sciences (Radiology, Biomedical) Machine Learning 7 Motivation • Imaging sciences is experiencing a tremendous growth in the U.S. The NYT recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as the primary reason for the growth. 8 Motivation • Imaging sciences is experiencing a tremendous growth in the U.S. The NYT recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as the primary reason for the growth.
    [Show full text]
  • Lars Ruthotto
    Lars Ruthotto 400 Dowman Dr, W408 [email protected] 30322 Atlanta, GA http://www.mathcs.emory.edu/~lruthot/ United States @lruthotto Education Doctor of Natural Sciences, University of M¨unster,Germany, 2012, thesis entitled Hyperelastic Image Registration - Theory, Numerical Methods, and Applications, advisors: Martin Burger and Jan Modersitzki. Diploma in Mathematics, University of M¨unster,Germany, 2010. Full-time Positions Associate Professor, Departments of Mathematics and Computer Science, Emory University, since Sept. 2020. Assistant Professor, Departments of Mathematics and Computer Science, Emory University, Sept. 2014 { Aug. 2020. Postdoctoral Research Fellow, Department of Mathematics / Earth and Ocean Sciences, University of British Columbia, Canada, Jan. 2013 { Aug. 2014. Research and Teaching Assistant, Institute of Mathematics and Image Computing, University of L¨ubeck, Germany, Oct. 2010 { Dec. 2012. Research Assistant, Institute of Biomagnetism and Biosignalanalysis, University of M¨unster,Germany, Aug. and Sept. 2010. Research and Teaching Assistant, Institute for Computational and Applied Mathematics, University of M¨unster,Germany, April 2010 { July 2010. Other Positions Senior Fellow, Institute for Pure and Applied Mathematics (IPAM), Long program on Machine Learning for Physics and the Physics of Learning Tutorials, Sept. 2019 { Dec. 2019. Senior Consultant, XTract Technologies Inc., Jan. 2017 { Sept. 2019. Publications Submitted Papers and Preprints [P.6] Onken D, Nurbekyan L, Li X, Wu Fung S, Osher S, Ruthotto
    [Show full text]
  • Lecture 1: Introduction to Medical Image Computing
    1 MEDICAL IMAGE COMPUTING (CAP 5937)- SPRING 2016 LECTURE 1: Introduction Dr. Ulas Bagci HEC 221, Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), Orlando, FL 32814. [email protected] or [email protected] 2 • This is a special topics course, offered for the first time in UCF. Lorem Ipsum Dolor Sit Amet CAP5937: Medical Image Computing 3 • This is a special topics course, offered for the first time in UCF. • Lectures: Mon/Wed, 10.30am- 11.45am Lorem Ipsum Dolor Sit Amet CAP5937: Medical Image Computing 4 • This is a special topics course, offered for the first time in UCF. • Lectures: Mon/Wed, 10.30am- 11.45am • Office hours: Lorem Ipsum Dolor Sit Amet Mon/Wed, 1pm- 2.30pm CAP5937: Medical Image Computing 5 • This is a special topics course, offered for the first time in UCF. • Lectures: Mon/Wed, 10.30am-11.45am • Office hours: Mon/Wed, 1pm- 2.30pm Lorem Ipsum Dolor Sit Amet • No textbook is required, materials will be provided. CAP5937: Medical Image Computing 6 Image Processing Computer Vision Medical Image Imaging Computing Sciences (Radiology, Biomedical) Machine Learning 7 Motivation • Imaging sciences is experiencing a tremendous growth in the U.S. The NYT recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as the primary reason for the growth. 8 Motivation • Imaging sciences is experiencing a tremendous growth in the U.S. The NYT recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as the primary reason for the growth.
    [Show full text]
  • Multimodal Neuroimaging Computing: the Workflows, Methods, and Platforms
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Sydney eScholarship Brain Informatics (2015) 2:181–195 DOI 10.1007/s40708-015-0020-4 Multimodal neuroimaging computing: the workflows, methods, and platforms Sidong Liu • Weidong Cai • Siqi Liu • Fan Zhang • Michael Fulham • Dagan Feng • Sonia Pujol • Ron Kikinis Received: 29 July 2015 / Accepted: 20 August 2015 / Published online: 4 September 2015 Ó The Author(s) 2015. This article is published with open access at Springerlink.com Abstract The last two decades have witnessed the 1 Introduction explosive growth in the development and use of noninva- sive neuroimaging technologies that advance the research Neuroimaging has profoundly advanced neuroscience on human brain under normal and pathological conditions. research and clinical care rapidly in the past two decades, Multimodal neuroimaging has become a major driver of prominently by magnetic resonance imaging (MRI), com- current neuroimaging research due to the recognition of the plemented positron emission tomography (PET), and clinical benefits of multimodal data, and the better access electroencephalography (EEG)/magnetoencephalography to hybrid devices. Multimodal neuroimaging computing is (MEG). The art of neuroimaging today is shaped by three very challenging, and requires sophisticated computing to concurrent, interlinked technological developments [1]: address the variations in spatiotemporal resolution and Data Acquisition The advances of imaging instrumen- merge the biophysical/biochemical information. We review tation have enabled digital image acquisition, as well as the current workflows and methods for multimodal neu- electronic data storage and communication systems, such roimaging computing, and also demonstrate how to con- as the picture archiving and communication system duct research using the established neuroimaging (PACS).
    [Show full text]