Fully Automated Segmentation Using Distance Regularised Level Set and Deep-Structured Learning and Inference

Abstract

We introduce a new segmentation methodology that combines the structured output inference from deep belief networks and the delineation from level set methods to produce accurate segmentation of anatomies from medical images. Deep belief networks can be used in the implementation of accurate segmentation models if large annotated training sets are available, but the limited availability of such large datasets in medical image analysis problems motivates the development of methods that can circumvent this demand. In this chapter, we propose the use of level set methods containing several shape and appearance terms, where one of the terms consists of the result from the deep belief network. This combination reduces the demand for large annotated training sets from the deep belief network and at the same time increases the capacity of the level set method to model more effectively the shape and appearance of the visual object of interest. We test our methodology on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2009 left ventricle segmentation challenge dataset and on Japanese Society of Radiological Technology (JSRT) lung segmentation dataset, where our approach achieves the most accurate results of the field using the semi-automated methodology and state-of-the-art results for the fully automated challenge.