Segmentation label propagation using deep convolutional neural networks and dense conditional random field

Mingchen Gao, Ziyue Xu, Le Lu, Aaron Wu, Isabella Nogues, Ronald M. Summers, Daniel J. Mollura

Abstract: Availability and accessibility of large-scale annotated medical image datasets play an essential role in robust supervised learning of medical image analysis. Missed labeling of regions of interest is a common issue on existing medical image datasets due to the labor intensive nature of the annotation task which requires high levels of clinical proficiency. In this paper, we present a segmentation based label propagation method to a publicly available dataset on interstitial lung disease [3], to address the missing annotation challenge. Upon validation from an expert radiologist, the amount of available annotated training data is largely increased. Such a dataset expansion can can potentially increase the accuracy of Computer-aided Detection (CAD) systems. The proposed constrained segmentation propagation algorithm combines the cues from the initial annotations, deep convolutional neural networks and a dense fully-connected Conditional Random Field (CRF) that achieves high quantitative accuracy levels.