ECE6690 BM5780 Computer Analysis of Biomedical Images  

Spring 2024

Lectures: Tuesday, and Thursday  10:10 - 11:25 am, Phillips 213

Instructor: Anthony P. Reeves

Introduction

A variety of powerful imaging modalities with attending computer image processing methods are available for the evaluation of health and the detection of disease. This course will focus on the quantitative analysis of these images and Computer Aided Diagnosis (CAD); that is, the automatic identification and classification of medical abnormalities by the computer from image data. The confluence of new technology providing more and higher resolution images together with policies for providing low cost non-invasive diagnostic methods is producing an imperative for the development of CAD. Landmark commercial CAD systems have been developed for cell analysis, mammography, and diabetic retinopathy; many more applications are in development. This methodology may also be applied to research in the life sciences where evaluation of biological processes and events may be achieved through observations and analysis of image data. In this context, microscopy is the most frequently used imaging modality.

Biomedical image analysis extends conventional computer vision methods in novel directions. Traditional computer vision methods have their foundation in industrial vision applications where the primary modality is the lens based video camera that provides two-dimensional projection images of a three-dimensional scene. However, many biomedical image modalities such as MR, CT, ultrasound, and light microscopy, have the ability to directly acquire true three-dimensional images. Consequently, three-dimensional (and four-dimensional with time) computer vision algorithms will be studied in detail.

Recently, there have been remarkable developments in the use of “AI” in the form of deep learning for computer vision applications. Much of the current research in the medical imaging area is focused on the deep learning method. Deep learning approaches for biomedical image analysis will be addressed in this course and a basic understanding of the deep learning method will be assumed.

Contact Information

The primary information source for ECE5780 will be the Cornell Canvas system.  Lecture notes and other reference material will be maintained on the course website at the following URL:

              https://www.via.cornell.edu/ece6690

The course instructor is Anthony P. Reeves. Contact information: Office, Rhodes Hall Rm 392; Phone 255 2342; email, reeves@cornell.edu. To meet individually with Professor Reeves you should schedule an appointment. The best way to do this is either to arrange a time with him at the end of a class, or to arrange a zoom meeting by email.

Organization and Content

This course has been offered a number of times and each time there has been a significant difference in the organization. The newness of the topic precludes a set formal outline and each semester new topics are explored through the means of course projects. The following is based on the last offering of this course in the Spring of 2022; some changes may be made this semester depending upon the makeup of the class. A continuing theme for the course is to allow entrance from two groups of students with different backgrounds: (a) engineers and computer scientists with experience in image analysis and (b) biologists, engineers, etc. with experience in the life sciences with or without any image analysis experience.

Prerequisites

This course is open to students with either a biology, computer science, or engineering background. The course will provide the necessary background on the imaging modalities, the medical issues, and the computer algorithms for image analysis. Engineering students should have some prior experience in image analysis;  having taken either ECE 5470 or the three-credit version of BME 6180 would be a very good preparation for this course. Life scientists, with a significant experience in a relevant topic area may also take this course even without a strong engineering background. Expected previous experience will be discussed on the first class.

This course is intended to meet the needs of both engineers interested in the life sciences and life scientists that are interested in gaining experience in quantitative image analysis methods. Class participation and project research is important. This is a graduate level course and, although it does not have demanding prerequisites, an active interest in biomedical imaging, the maturity to identify and the address any specific knowledge shortcomings, and active class participation are expected.

Course Text

There no single required course text. Reference texts, book chapters, and research papers will be made available on-line through the course website.

The Class Project

The class project is done in groups of at least four students. The project typically consists of background research, and the implementation and evaluation of a specific image analysis method on appropriate image data. Short class presentations of the project proposal and the final project outcome may be required. Each group explores a different problem to provide the broadest experience to the class.

Course Objectives

Students having either an engineering or life science background will gain an understanding of the state of the art in the computer analysis of biomedical images. They will have a good knowledge of the image processing algorithms, the essential characteristics of the main image modalities, and the statistical methods to analyze and validate CAD systems. Through the course project students will gain experience in designing and validating computer algorithms for image analysis and in presenting this work to the class audience in a succinct and professional manner.

Academic Integrity

Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student's own work. For this course collaboration is allowed in the following instance: the class project.

See also ECE5470 - Computer Vision offered in the fall.