Bayesian Logistic Shape Model Inference: application to cochlea image segmentation

TL;DR

This paper introduces a Bayesian shape inference framework for medical image segmentation, specifically applied to cochlea CT images, combining shape and appearance information for interpretable results.

Contribution

It presents a novel Bayesian inference method for parametric shape models using an EM approach with shape-appearance integration, applied to cochlea segmentation.

Findings

Performance comparable to supervised methods

Outperforms previous unsupervised approaches

Allows analysis of shape parameter distributions and uncertainty

Abstract

Incorporating shape information is essential for the delineation of many organs and anatomical structures in medical images. While previous work has mainly focused on parametric spatial transformations applied on reference template shapes, in this paper, we address the Bayesian inference of parametric shape models for segmenting medical images with the objective to provide interpretable results. The proposed framework defines a likelihood appearance probability and a prior label probability based on a generic shape function through a logistic function. A reference length parameter defined in the sigmoid controls the trade-off between shape and appearance information. The inference of shape parameters is performed within an Expectation-Maximisation approach where a Gauss-Newton optimization stage allows to provide an approximation of the posterior probability of shape parameters. This framework is applied to the segmentation of cochlea structures from clinical CT images constrained by a 10 parameter shape model. It is evaluated on three different datasets, one of which includes more than 200 patient images. The results show performances comparable to supervised methods and better than previously proposed unsupervised ones. It also enables an analysis of parameter distributions and the quantification of segmentation uncertainty including the effect of the shape model.