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Guest Editor's Introduction: Imaging in Medical Education

Parvati Dev, Stanford University

Pages: p. 20

The clinical encounter—an interactive and highly visual process—begins as the physician enters the door and analyzes the patient's appearance, facial appearance, and posture. Even before entering the examination room, the physician reads the patient folder, which may include x-ray images or reports of earlier imaging procedures. She may order tests, including a variety of radiographic procedures, photographs of skin lesions, or biopsies, which are photographed under a microscope. If the patient needs surgery, both preoperative and postoperative surgical photographs may be acquired.

These and other images form an essential component of the learning process in medical education. Beginning with Introduction to Human Anatomy, students examine images and drawings of dissections as they work with human cadavers to learn the structure of the human body. Through histological images, photographed through the microscope at different magnifications and highlighted with a variety of tissue stains, they learn the microscopic structure of tissues. As they study the many causes of infectious diseases, they examine bacteria, viruses, parasites, and fungi under the microscope. They examine vast libraries of pathology images, studying cancerous tissue, benign growths, and organs damaged by trauma. They learn to examine the human body through many different imaging modalities that let them peer into the living human: x-rays, computed tomography scans, magnetic resonance images, and images created by radiation from isotopes that bind to different tissues.

This special issue presents a few of the many ways in which medical teaching today uses imaging and imaging research.

Understanding the structure of the human body underlies much of medical education. Three of the articles address issues in understanding and teaching anatomy and neuroanatomy. Brinkley, Rosse, and others have worked extensively on developing computer models of organs and representing these organs in relationship to each other. For teaching, they have annotated their models and provided interactive access to their collection over the Web. In this issue, they present a knowledge representation infrastructure that will become essential as large collections of anatomy models develop.

Senger, on the other hand, focuses on the problem of extracting the outlines of anatomical structures from a stack of cross-sectional images. His immersive approach to segmentation builds on the hypothesis that an interplay exists between how a person looks at the data and his ability to perceive and extract boundaries and object shapes. He encourages a constructive learning approach by asking students to learn anatomy by segmenting the slices themselves.

Zhou, Thompson, and Toga present an algorithm for automatic extraction of one of the key neuroanatomic features, the sulci of the brain. Their article represents the trend towards the development of specialized algorithms for extraction of different anatomic structures.

The tutorial following this introduction gives an overview of the many types of imagery encountered in the process of learning medicine. They range from images of dissections that would look familiar to medical practitioners a century ago to images of pre- and postsurgical plans of vascular repair based on computer models of the arteries of individual patients. A sidebar by Summers shows how in the future the Virtual Reality Modeling Language (VRML) will assist viewing and interacting with 3D models of clinical anatomy. The article by Wong and Soo Hoo presents a complementary view, showing the system, transmission, and storage needs as hospital-based imagery becomes digital and accessed from centrally administered archival systems.

Together, the articles in this issue present a picture of imagery's role in medical education and the many research topics needing investigation as computers play an increasing role in this education process.

About the Authors

Bio Graphic
Parvati Dev is director of the educational technology development group, Summit—Stanford University Medical Media and Information Technologies. The digital educational environments under her direction range from Web-based content and administration support for every preclinical medical course at Stanford's School of Medicine to simulation-based and virtual environments for learning physiology and clinical encounters. She has a PhD in electrical engineering from Stanford University and a BTech from the Indian Institute of Technology, Kharagpur, India. She is a member of the IEEE, the ACM, the American Medical Informatics Association, and the Society for Neuroscience.
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