Deep learning-based framework for cardiac function assessment in embryonic zebrafish from heart beating videos
Abstract
In this thesis, application of imaging in assessment of zebrafish (zf) cardiology is discussed. Medical imaging seeks to expose internal structures, as well as to diagnose and treat disease. Medical imaging also establishes a database of normal anatomy and physiology to make it possible to identify abnormalities. Considering that the embryonic zebrafish is transparent, bright field microscopic videos could reveal heart mechanism and could be useful for quantification of it, although microscopic imaging can be useful for adult zebrafish as well. Different imaging methods used in other works is discussed first. Later, the cardiovascular parameters that can be measured using imaging are defined. We then compare different digital image processing and deep learning algorithms that have been employed to process or segment images from zebrafish. At the end of the chapter challenges mutant type are investigated.