Ricci Curvature Based Volumetric Segmentation of the Auditory Ossicles

TL;DR

This paper introduces an automatic, curvature-based segmentation method for auditory ossicles in CT images, eliminating manual initialization and improving accuracy over existing techniques.

Contribution

It proposes a novel Ricci curvature term in the energy function for automatic ossicle localization and segmentation, outperforming state-of-the-art methods.

Findings

Ricci curvature improves segmentation accuracy

Automatic localization without manual labels

Outperforms existing methods in experiments

Abstract

The auditory ossicles that are located in the middle ear are the smallest bones in the human body. Their damage will result in hearing loss. It is therefore important to be able to automatically diagnose ossicles' diseases based on Computed Tomography (CT) 3D imaging. However CT images usually include the whole head area, which is much larger than the bones of interest, thus the localization of the ossicles, followed by segmentation, both play a significant role in automatic diagnosis. The commonly employed local segmentation methods require manually selected initial points, which is a highly time consuming process. We therefore propose a completely automatic method to locate the ossicles which requires neither templates, nor manual labels. It relies solely on the connective properties of the auditory ossicles themselves, and their relationship with the surrounding tissue fluid. For the segmentation task, we define a novel energy function and obtain the shape of the ossicles from the 3D CT image by minimizing this new energy. Compared to the state-of-the-art methods which usually use the gradient operator and some normalization terms, we propose to add a Ricci curvature term to the commonly employed energy function. We compare our proposed method with the state-of-the-art methods and show that the performance of discrete Forman-Ricci curvature is superior to the others.