Deep Learning Techniques for Medical Image Segmentation and Object Recognition

Abstract

Segmentation of a target object in the form of closed curves has many potential applications in medical imaging because it provides quantitative information related to its size and shape. Over the last few decades, many innovative methods of performing segmentation have been proposed, and these segmentation techniques are based on the basic recipes using thresholding and edge-based detection. Segmentation and classification in medical imaging are in fact experiencing a paradigm shift due to a marked and rapid advance in deep learning (DL) techniques. DL methods have nonlinear representability to extract and utilize global spatial features and local spatial features simultaneously, showing amazing overall performance in medical image segmentation. DL methods mostly lack transparency due to the black-box output, so clinicians cannot trace the output back to present the causal relationship of the output diagnosis. Therefore, in order to safely utilize DL algorithms in the medical field, it is desirable to design the models to transparently explain the reason for making the output diagnosis rather than a black-box. For explainable DL, a systematic study is needed to rigorously analyze which input characteristics affect the output of the network. Despite the lack of rigorous analysis in DL, recent rapid advances indicate that DL algorithms will improve their performance as training data and experience accumulate over time.