Introduction to Medical Image Computing
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Bio 219 Biomedical Imaging and Scientific Visualization
Bio 219 Biomedical Imaging and Scientific Visualization Ch. Zollikofer M. Ponce de León Organization • OLAT: – course scripts (pdf) • website: www.aim.uzh.ch/morpho/wiki/Teaching/SciVis – course scripts (pdf); passwd: scivisdocs – link collection (tutorials, applets, software/data downloads, ...) • book (background information): Zollikofer & Ponce de León, Virtual Reconstruction. A Primer in Computer-assisted Paleontology and Biomedicine (NY: Wiley, 2005) CHF 55 • final exam – Monday, 26. May 2014, 1015-1100 BioMedImg & SciVis • at the intersection between – theory/practice of image data acquisition – computer graphics – medical diagnostics – computer-assisted paleoanthropology Grotte Chauvet, France Biomedical Imaging • acquisition • processing • analysis • visualization ... of biomedical data Scientific Visualization visual... • representation (cf. data presentation) • exploration • analysis ...of scientific data aims of this course • provide theoretical (and practical) foundations of – image data acquisition, storage, retrieval – image data processing and analysis – image data visualization/rendering • establish links between – real-life vision and computer vision – computer science and biomedical sciences – theory and practice of handling biomedical data contents • real-life vision • computers and data representation • 2D image data acquisition • 3D image data acquisition • biomedical image processing in 2D and 3D • biomedical image data visualization and interaction biomedical data types of data data flow humans and computers facts and data • facts exist by definition (±independent of the observer): – females and males – humans and Neanderthals – dogs and wolves • data are generated through observation: – number of living human species: 1 – proportion of females to males at birth: 49/51 – nr. of wolves per square km biomedical data: general • physical/physiological data about the human body: – density – temperature – pressure – mass – chemical composition biomedical data: space and time • spatial – 1D – 2D – 3D • temporal • spatiotemporal (4D) .. -
Three-Dimensional Thematic Map Imaging of the Yacht Port on the Example of the Polish National Sailing Centre Marina in Gda ´Nsk
applied sciences Article Three-Dimensional Thematic Map Imaging of the Yacht Port on the Example of the Polish National Sailing Centre Marina in Gda ´nsk Pawel S. Dabrowski 1 , Cezary Specht 1,* , Mariusz Specht 2 and Artur Makar 3 1 Department of Geodesy and Oceanography, Gdynia Maritime University, 81-347 Gdynia, Poland; [email protected] 2 Department of Transport and Logistics, Gdynia Maritime University, 81-225 Gdynia, Poland; [email protected] 3 Department of Navigation and Hydrography, Polish Naval Academy, 81-127 Gdynia, Poland; [email protected] * Correspondence: [email protected] Abstract: The theory of cartographic projections is a tool which can present the convex surface of the Earth on the plane. Of the many types of maps, thematic maps perform an important function due to the wide possibilities of adapting their content to current needs. The limitation of classic maps is their two-dimensional nature. In the era of rapidly growing methods of mass acquisition of spatial data, the use of flat images is often not enough to reveal the level of complexity of certain objects. In this case, it is necessary to use visualization in three-dimensional space. The motivation to conduct the study was the use of cartographic projections methods, spatial transformations, and the possibilities offered by thematic maps to create thematic three-dimensional map imaging (T3DMI). The authors presented a practical verification of the adopted methodology to create a T3DMI visualization of Citation: Dabrowski, P.S.; Specht, C.; Specht, M.; Makar, A. the marina of the National Sailing Centre of the Gda´nskUniversity of Physical Education and Sport Three-Dimensional Thematic Map (Poland). -
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. -
Members | Diagnostic Imaging Tests
Types of Diagnostic Imaging Tests There are several types of diagnostic imaging tests. Each type is used based on what the provider is looking for. Radiography: A quick, painless test that takes a picture of the inside of your body. These tests are also known as X-rays and mammograms. This test uses low doses of radiation. Fluoroscopy: Uses many X-ray images that are shown on a screen. It is like an X-ray “movie.” To make images clear, providers use a contrast agent (dye) that is put into your body. These tests can result in high doses of radiation. This often happens during procedures that take a long time (such as placing stents or other devices inside your body). Tests include: Barium X-rays and enemas Cardiac catheterization Upper GI endoscopy Angiogram Magnetic Resonance Imaging (MRI) and Magnetic Resonance Angiography (MRA): Use magnets and radio waves to create pictures of your body. An MRA is a type of MRI that looks at blood vessels. Neither an MRI nor an MRA uses radiation, so there is no exposure. Ultrasound: Uses sound waves to make pictures of the inside of your body. This test does not use radiation, so there is no exposure. Computed Tomography (CT) Scan: Uses a detector that moves around your body and records many X- ray images. A computer then builds pictures or “slices” of organs and tissues. A CT scan uses more radiation than other imaging tests. A CT scan is often used to answer, “What does it look like?” Nuclear Medicine Imaging: Uses a radioactive tracer to produce pictures of your body. -
(NSPM) Content Outline and Exam Blueprint Effective Through 2020
Nonsurgical Pain Management (NSPM) Content Outline and Exam Blueprint Effective through 2020 The NSPM subspecialty examination will assess a nurse anesthetist’s competence of needle placement in three anatomical approaches (i.e., midline, lateral, peripheral) and four anatomical regions (i.e., cervical, thoracic, lumbar, and sacral), as well as assess knowledge related to the NSPM certification examination content outline as listed below: Domain I. Physiology and Pathophysiology of Pain (13%) I.A. Applicable anatomy and physiology of pain I.B. Nociception: transduction, transmission, perception and modulation of pain I.C. Factors influencing pain I.D. Cellular response to pain and treatment I.E. Pain classifications I.F. Evidence based principles I.G. Pathophysiology I.G.1. Neurotransmitters I.G.2. Inflammatory mediators I.G.3. Pain pathways Domain II. Imaging Safety (8%) II.A. Evaluation of equipment II.B. Equipment safety II.C. Radiation safety II.D. Safe practices with imaging equipment II.E. Provider and Staff safety II.F. Patient safety Domain III. Assessment/Diagnosis/Integration/Referral (24%) III.A. Physical examination III.B. Pain generators III.C. Health history III.D. Diagnostic studies III.D.1. MRI III.D.2. CT III.D.3. EMG III.D.4. X-ray III.D.5. Discogram III.E. Documentation III.F. Data interpretation III.G. Treatment plan III.H. Imaging strategies III.I. Clinical judgment III.J. Multidisciplinary collaboration 1 Nonsurgical Pain Management (NSPM) Content Outline and Exam Blueprint Effective through 2020 Domain IV. Pharmacological Treatment (15%) IV.A. Pharmacology and pain IV.A.1. NSAIDS IV.A.2. Opioids IV.A.3. -
Imaging Services Order Guide Medicare Guidelines Require Explicit Written and Signed Provider Orders
Imaging Services 2021 Imaging Services Order Guide Medicare guidelines require explicit written and signed provider orders. This guide was created to assist you in ordering and authorizing exams accurately. Please obtain insurance authorizations before scheduling imaging studies. Call one of our licensed/certified technologists if you have questions or comments. NOTE: Please DO NOT call the department to schedule an appointment. CENTRALIZED SCHEDULING . 509-248-9592 GENERAL X-RAY . 509-895-0509 COMPUTED TOMOGRAPHY (CT) . 509-895-0507 MAGNETIC RESONANCE IMAGING (MRI) . 509-895-0505 NUCLEAR MEDICINE . 509-575-8099 `OHANA MAMMOGRAPHY . 509-574-3863 ULTRASOUND . 509-249-5154 VASCULAR ULTRASOUND–Memorial Heart & Vascular . 509-574-0243 VASCULAR STAT REFERRALS–Memorial Heart & Vascular . .509-494-0551 CARDIOVASCULAR SERVICES–Memorial Heart & Vascular . 509-574-0243 BONE DENSITY–Lakeview Campus . 509-972-1170 PROVIDER EMERGENCY . 509-248-7380 Option 0 Consultation of a Clinical Decision Support Mechanism is required to determine if advanced diagnostic imaging services (CT, MRI, Nuclear Medicine, PET) adheres to Appropriate Use Criteria. Order must include Decision Support Number (DSN), G-Code, and Modifier. PHONE | 509-895-0507 PHONE | 509-895-0507 2 3 CT/CAT Scan/Computed Tomography FAX | 509-576-6982 CT/CAT Scan/Computed Tomography FAX | 509-576-6982 *If patient is over 400 lbs., please call the CT department at 509-895-0507. *If patient is over 400 lbs., please call the CT department at 509-895-0507. Consultation of a Clinical Decision Support Mechanism is required. Order must include DSN, G-Code, and Modifier. Consultation of a Clinical Decision Support Mechanism is required. Order must include DSN, G-Code, and Modifier. -
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%). -
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. -
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. -
Pyvista: Managing & Visualizing Geospatial Data Using an Open
PYVISTA: MANAGING & VISUALIZING GEOSPATIAL DATA USING AN OPEN-SOURCE FRAMEWORK by C. Bane Sullivan c Copyright by C. Bane Sullivan, 2020 All Rights Reserved A thesis submitted to the Faculty and the Board of Trustees of the Colorado School of Mines in partial fulfillment of the requirements for the degree of Master of Science (Hydrol- ogy). Golden, Colorado Date Signed: C. Bane Sullivan Signed: Dr. Whitney J. Trainor-Guitton Thesis Advisor Golden, Colorado Date Signed: Dr. Josh Sharp Professor and Director Hydrologic Science & Engineering Program ii ABSTRACT There is a wide range of data types present in typical hydrogeophysical studies; being able to gather all data types for a given project into a single framework is challenging and often unachievable at an affordable cost for hydrological researchers. Steep licensing fees for commercial software and complex user interfaces in existing open software have limited the accessibility of tools to build, integrate, and make decisions with diverse types of 3D geospa- tial data and models. In earth science research, particularly in hydrology, restricted budgets exacerbate these limitations, creating barriers for using software to manage, visualize, and exchange 3D data in reproducible workflows. In response to these challenges, I have created the PyVista software as an open-source framework for 3D geospatial data management, fu- sion, and visualization. The PyVista Python package provides an accessible and intuitive interface back to a robust and established visualization library, the Visualization Toolkit (VTK), to facilitate rapid analysis and visual integration of spatially referenced datasets. This interface implements spatial data structures encompassing a majority of subsurface applications to make creating, managing, and analyzing spatial data more streamlined for domain scientists. -
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. -
2021-2022 Diagnostic Imaging and Therapy Information Packet
www.gatewayct.edu DIAGNOSTIC IMAGING & THERAPY PROGRAMS INFORMATION PACKET 2021-2022 Academic Year Diagnostic Medical Sonography Nuclear Medicine Technology Radiation Therapy Radiography Rev. 06/20 Please disregard all previous versions of the Diagnostic Imaging & Therapy Information Packet Please note: Information in this packet is subject to change. If you do not intend to apply to one of the Diagnostic Imaging & Therapy Programs for the 2021-2022 academic year, please obtain an updated packet for future years. 1 of 23 Introduction Diagnostic Imaging & Therapy refers to four disciplines: Diagnostic Medical Sonography (Associate Degree) Nuclear Medicine Technology (Associate Degree and Certificate) Radiation Therapy (Associate Degree) Radiography (Associate Degree) Diagnostic Medical Sonography The Associate in Science degree program in Diagnostic Medical Sonography (DMS) offers the student an outstanding opportunity to acquire both the academic and technical skills necessary to perform abdominal, obstetrical, superficial, vascular and gynecological sonography procedures. Students will train with highly skilled Sonographers at leading healthcare facilities. Graduates are encouraged to apply for National Qualifying Examination for certification in Sonography with The American Registry of Diagnostic Medical Sonography (ARDMS) (www.ardms.org) and/or the American Registry of Radiologic Technologists (ARRT (S)) (www.arrt.org). The DMS program is accredited in General (Abdomen and OBGYN) and Vascular concentrations by the Commission on Education of Allied Health Education Programs, 25400 US Highway 19 North, Suite 158, Clearwater, FL 33763, P:727-210-2350 F:727-210-2354, E: [email protected]. The joint committee on Education in Diagnostic Medical Sonography (JRC_DMS) is a nonprofit organization in existence to establish, maintain and promote quality standards for educational programs in DMS.