Learning Shape and Texture Characteristics of CT Tree-in-Bud Opacities for CAD Systems

TL;DR

This paper introduces two novel methods for automatically detecting Tree-in-bud patterns in CT scans, which are indicative of respiratory infections, achieving high accuracy in a challenging detection task.

Contribution

The paper presents two innovative techniques for TIB pattern detection, including a fast localization method and a M"{o}bius invariant feature extraction approach, advancing CAD capabilities.

Findings

Achieved an overall detection accuracy of 90.96%.

Demonstrated effective detection on a dataset of viral bronchiolitis CTs.

Proposed methods outperform existing approaches in TIB detection.

Abstract

Although radiologists can employ CAD systems to characterize malignancies, pulmonary fibrosis and other chronic diseases; the design of imaging techniques to quantify infectious diseases continue to lag behind. There exists a need to create more CAD systems capable of detecting and quantifying characteristic patterns often seen in respiratory tract infections such as influenza, bacterial pneumonia, or tuborculosis. One of such patterns is Tree-in-bud (TIB) which presents \textit{thickened} bronchial structures surrounding by clusters of \textit{micro-nodules}. Automatic detection of TIB patterns is a challenging task because of their weak boundary, noisy appearance, and small lesion size. In this paper, we present two novel methods for automatically detecting TIB patterns: (1) a fast localization of candidate patterns using information from local scale of the images, and (2) a M\"{o}bius invariant feature extraction method based on learned local shape and texture properties. A comparative evaluation of the proposed methods is presented with a dataset of 39 laboratory confirmed viral bronchiolitis human parainfluenza (HPIV) CTs and 21 normal lung CTs. Experimental results demonstrate that the proposed CAD system can achieve high detection rate with an overall accuracy of 90.96%.