Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing

TL;DR

This paper introduces a probabilistic framework using Bayesian smoothing to accurately track and segment tree-like structures such as airways in 3D medical images, improving robustness against noise and interference.

Contribution

It presents a novel exploratory tracking method with Bayesian smoothing for tree structures, outperforming traditional sequential methods in medical image analysis.

Findings

More branches detected compared to baseline

Robustness to noise and anomalies demonstrated

Effective discrimination of true and false branches

Abstract

Segmenting tree structures is common in several image processing applications. In medical image analysis, reliable segmentations of airways, vessels, neurons and other tree structures can enable important clinical applications. We present a framework for tracking tree structures comprising of elongated branches using probabilistic state-space models and Bayesian smoothing. Unlike most existing methods that proceed with sequential tracking of branches, we present an exploratory method, that is less sensitive to local anomalies in the data due to acquisition noise and/or interfering structures. The evolution of individual branches is modelled using a process model and the observed data is incorporated into the update step of the Bayesian smoother using a measurement model that is based on a multi-scale blob detector. Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother, which provides Gaussian density estimates of branch states at each tracking step. We select likely branch seed points automatically based on the response of the blob detection and track from all such seed points using the RTS smoother. We use covariance of the marginal posterior density estimated for each branch to discriminate false positive and true positive branches. The method is evaluated on 3D chest CT scans to track airways. We show that the presented method results in additional branches compared to a baseline method based on region growing on probability images.