Multimodal Neuroimaging Computing: the Workflows, Methods, and Platforms

Total Page:16

File Type:pdf, Size:1020Kb

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). These imaging systems, CT, MRI and PET computing packages and platforms. showed obvious clinical benefits by providing high contrast tissue differentiation. The previous film-based reading was Keywords Multimodal Á Neuroimaging Á Medical image replaced by the electronic displays (axial, coronal and computing sagittal planes of the volume) without losing diagnostic quality. Medical Image Computing The growth of neuroimaging has spurred a parallel development of neuroimaging com- puting methods and workflows, including bias correction, S. Liu (&) Á W. Cai Á S. Liu Á F. Zhang Á D. Feng registration, segmentation, information extraction and School of IT, The University of Sydney, Sydney, Australia visualization. We should note the difference between e-mail: [email protected] neuroimaging and neuroimaging computing. Neuroimag- F. Zhang Á S. Pujol Á R. Kikinis ing focuses on the image acquisition, capturing the snap- Surgical Planning Laboratory, Harvard Medical School, Boston, shot of the brain; whereas neuroimaging computing focuses USA on the computational analysis of the brain images, extracting and enhancing the information of relevance to M. Fulham Department of PET and Nuclear Medicine, Royal Prince Alfred best describe the brain anatomy and function. Hospital, Sydney Medical School, The University of Sydney, Package and Platform Development To fit into research Sydney, Australia and clinical timelines and facilitate translational medicine, the neuroimaging computing methods and workflows are D. Feng Med-X Research Institute, Shanghai Jiao Tong University, often integrated into software packages. Many such pack- Shanghai, China ages were added to imaging systems by the major vendors 123 182 S. Liu et al. of medical imaging equipment and many specialized Fig. 1 Overview of the current status and major components of c companies. However, a greater number of neuroimaging multimodal neuroimaging computing, including neuroimaging modalities, modality-specific computing workflows, multimodal computing packages and platforms are free and open- computing methods, algorithms, task-oriented packages, all-inte- source, designed and supported by the medical imaging grated platforms, and neuroimaging research communities research groups and communities. Multimodal neuroimaging, i.e., the simultaneous imag- ing measurement (EEG/fMRI [2], PET/CT [3]) or sum- including neuroimaging modalities, modality-specific mation of separate measurement (PET and sMRI [4], sMRI computing workflows (a series of tasks), multimodal com- and dMRI [5], fMRI and dMRI [6]), has become an puting methods, algorithms, packages, platforms and com- emerging research area due to better access to imaging munities. MRI, PET, EEG/MEG and their computing devices, especially the hybrid systems, such as PET/CT [7, workflows and methods are discussed in this review. A 8] and PET/MR [9]. The recent advances in neuroimaging neuroimaging computing task in an analysis workflow may computing methods also enabled joint analysis of the be fulfilled by multiple algorithms, and the most widely used multimodal data. The free and open-source software algorithms, e.g., voxel-based morphometry (VBM) [49], are (FOSS) packages and platforms for neuroimaging com- often integrated into software packages, e.g., Statistical puting further facilitate the translation of the multimodal Parametric Mapping (SPM)1, FMRIB Software Library neuroimaging research from the lab to better clinical care. (FSL)2, and Neurostat3. The new imaging tasks also demand Multimodal neuroimaging advances the neuroscience the refinement of existing algorithms and development of research by overcoming the limits of individual imaging new algorithms. Similar algorithms are often developed modalities and by identifying the associations of findings independently in different labs, sometimes with little from different imaging sources. Multimodal neuroimaging awareness of existing packages/platforms. has been used to investigate a multitude of populations and This paper is organized as follows. In Sect. 2,we disorders, such as Alzheimer’s disease (AD) [4, 10–12], elaborate the computing workflows, which consist of a schizophrenia [13–16], epilepsy [3, 17–19], obsessive- number of specific tasks, for individual modalities. In compulsive disorder (OCD) [20–22], bipolar disorder [23, Sect. 3, we review the major multimodal neuroimaging 24], attention-deficit hyperactivity disorder (ADHD) [25– computing methods, i.e., registration, segmentation, feature 27], Autism spectrum disorder (ASD) [28–30], traumatic integration, pattern analysis and visualization. In Sect. 4, brain injury (TBI) [31–34], stroke [35, 36], multiple scle- we introduce the task-oriented packages and platforms for rosis (MS) [37–39], and brain tumors [9, 40–42]. We have the tasks mentioned in previous sections. We focus on the recently reviewed advances in neuroimaging technologies free and open source software (FOSS) in this review, since and the applications of multimodal neuroimaging in these they could help to better realize the quickly evolved neuropsychiatric disorders [43]. Multimodal neuroimaging methods and workflows than their commercial counter- has also been used in many non-clinical applications, such parts, and thus accelerate translational medicine. For the as building brain machine interface (BMI) [44], tracing sake of clarity and precision, the algorithms, packages and neural activity pathways [45] and mapping mind and platforms are not described in detail, but we refer the behavior to brain anatomy [46–48]. interested readers to more specific papers instead. In Multimodal neuroimaging computing is a very chal- Sect. 5, we give one example of brain tumor surgical lenging task due to large inter-modality variations in spa- planning using the established packages and platforms. tiotemporal resolution, and biophysical/biochemical Lastly, we outline the future directions of multimodal mechanism. Compared to single imaging modality com- computing in Sect. 6. puting, it requires more sophisticated bias correction, co- registration, segmentation, feature extraction, pattern analysis, and visualization. Various methods for neu- 2 Modality-specific neuroimaging computing roimaging analysis have been proposed, and many have workflows been integrated into the task-oriented packages or inte- grated platforms. 2.1 Bias and artifacts correction In this paper, we review the state-of-the-art methods and workflows for both modality-specific neuroimaging com- Different neuroimaging techniques have different spa- puting and multimodal neuroimaging computing, and tiotemporal resolutions, and biophysical/biochemical demonstrate how to conduct multimodal neuroimaging research using the established packages and platforms. 1 www.fil.ion.ucl.ac.uk/spm. Fig. 1 provides an overview of the current status and illus- 2 www.fmrib.ox.ac.uk/fsl. trates the major components of neuroimaging computing, 3 http://128.208.140.75/*Download/. 123 Multimodal neuroimaging computing... 183 Modalities sMRI dMRI fMRI PET EEG / MEG Modality-Specific Bias and Artifacts Correction Computing Workflows Tensor / ODF ERP / ERF Skull Stripping Spatial Normalization Estimation Analysis Tissue / ROI Parametric Map Time / Frequency Spatial Smoothing Segmentation Reconstruction Analysis Surface Source Fiber Tracking Parameter Estimation Reconstruction Reconstruction Brain Connec Connectome Functional Brain Mapping Morphometry tome Registration Segmentation Features Fusion Pattern Analysis Visualization Multimodal Rigid Manual / Semi- Feature Multi-Dimensional Computing Methods Classification Registration Auto Outlining Selection
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]
  • 9. Biomedical Imaging Informatics Daniel L
    9. Biomedical Imaging Informatics Daniel L. Rubin, Hayit Greenspan, and James F. Brinkley After reading this chapter, you should know the answers to these questions: 1. What makes images a challenging type of data to be processed by computers, as opposed to non-image clinical data? 2. Why are there many different imaging modalities, and by what major two characteristics do they differ? 3. How are visual and knowledge content in images represented computationally? How are these techniques similar to representation of non-image biomedical data? 4. What sort of applications can be developed to make use of the semantic image content made accessible using the Annotation and Image Markup model? 5. Describe four different types of image processing methods. Why are such methods assembled into a pipeline when creating imaging applications? 6. Give an example of an imaging modality with high spatial resolution. Give an example of a modality that provides functional information. Why are most imaging modalities not capable of providing both? 7. What is the goal in performing segmentation in image analysis? Why is there more than one segmentation method? 8. What are two types of quantitative information in images? What are two types of semantic information in images? How might this information be used in medical applications? 9. What is the difference between image registration and image fusion? Given an example of each. 1 9.1. Introduction Imaging plays a central role in the healthcare process. Imaging is crucial not only to health care, but also to medical communication and education, as well as in research.
    [Show full text]