Structure-constrained deep feature fusion for chronic otitis media and cholesteatoma identification

Abstract

Chronic suppurative otitis media (CSOM) and middle ear cholesteatoma (MEC) were two most common chronic middle ear disease(MED) clinically. Accurate differential diagnosis between these two diseases is of high clinical importance given the difference in etiologies, lesion manifestations and treatments. The high-resolution computed tomography (CT) scanning of the temporal bone presents a better view of auditory structures, which is currently regarded as the first-line diagnostic imaging modality in the case of MED. In this paper, we first used a region-of-interest (ROI) network to find the area of the middle ear in the entire temporal bone CT image and segment it to a size of 100*100 pixels. Then, we used a structure-constrained deep feature fusion algorithm to convert different characteristic features of the middle ear in three groups as suppurative otitis media (CSOM), middle ear cholesteatoma (MEC) and normal patches. To fuse structure information, we introduced a graph isomorphism network that implements a feature vector from neighbourhoods and the coordinate distance between vertices. Finally, we construct a classifier named the “otitis media, cholesteatoma and normal identification classifier” (OMCNIC). The experimental results achieved by the graph isomorphism network revealed a 96.36% accuracy in all CSOM and MEC classifications. The experimental results indicate that our structure-constrained deep feature fusion algorithm can quickly and effectively classify CSOM and MEC. It will help otologist in the selection of the most appropriate treatment, and the complications can also be reduced.