Bayesian Logistic Shape Model Inference: application to cochlea image segmentation
This work addresses the need for interpretable and uncertainty-aware segmentation of anatomical structures in medical imaging, though it is incremental as it builds on existing shape model methods.
The authors tackled the problem of segmenting cochlea structures in medical CT images by developing a Bayesian inference framework for parametric shape models, achieving performance comparable to supervised methods and better than previous unsupervised approaches on datasets including over 200 patient images.
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.