Automatic Detection and Quantification of Tree-in-Bud (TIB) Opacities From CT Scans

Abstract

This study presents a novel computer-assisted detection (CAD) system for automatically detecting and precisely quantifying abnormal nodular branching opacities in chest computed tomography (CT), termed tree-in-bud (TIB) opacities by radiology literature. The developed CAD system in this study is based on 1) fast localization of candidate imaging patterns using local scale information of the images, and 2) Möobius invariant feature extraction method based on learned local shape and texture properties of TIB patterns. For fast localization of candidate imaging patterns, we use ball-scale filtering and, based on the observation of the pattern of interest, a suitable scale selection is used to retain only small size patterns. Once candidate abnormality patterns are identified, we extract proposed shape features from regions where at least one candidate pattern occupies. The comparative evaluation of the proposed method with commonly used CAD methods is presented with a dataset of 60 chest CTs (laboratory confirmed 39 viral bronchiolitis human parainfluenza CTs and 21 normal chest CTs). The quantitative results are presented as the area under the receiver operator characteristics curves and a computer score (volume affected by TIB) provided as an output of the CAD system. In addition, a visual grading scheme is applied to the patient data by three well-trained radiologists. Inter-observer and observer–computer agreements are obtained by the relevant statistical methods over different lung zones. Experimental results demonstrate that the proposed CAD system can achieve high detection rates with an overall accuracy of 90.96%.Moreover, correlations of observer–observer (R2 = 0.8848,p <0.01) and observer–CAD agreements (R2 = 0.824,p <0.01) validate the feasibility of the use of the proposed CAD system in detecting and quantifying TIB patterns.

Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends

“…Machine learning-based methods are useful for detecting and quantifying pathologic conditions; hence, these methods are considered to be the core part of the lung segmentation process as it examines every single voxel on the CT image and results in both lung and pathologic areas in the same framework (55)(56)(57)(58)(59). However, the main disadvantage of machine learning-based approaches is that depending on the complexity of the feature set, these approaches are computationally expensive and usually cannot model structural information (such as global shape or appearance information of the lungs) because only small patches are considered as features to be submitted into the classifiers.…”

A Generic Approach to Pathological Lung Segmentation

“…Extracted features vary depending on the imaging modality and the body region. A number of feature sets for classification of lung pathologies have been proposed: 3D adaptive multiple feature method (AMFM) [24], texton-based approach [25], intensity-based features [26], gray level co-occurrence matrix (GLCM) [27], wavelet and Gabor transform [28], shape and context-based attributes [29], [30], local binary patterns (LBP) [31], and histogram of gradients (HOG) [29]. The most challenging aspect of these approaches is the selection of feature set appropriate for the task at hand, which is still an active area of research, however.…”

A computational pipeline for quantification of pulmonary infections in small animal models using serial PET-CT imaging

“…To the best of our knowledge, there is no systematic study exploring this longitudinal phenomena in human clinical trials yet. Based on our previous research [44-46] and some other works showing the imaging findings of pulmonary infections, there are certain similarities in image analysis of pre-clinical and clinical subjects. Consolidations and GGOs are the two main imaging patterns from CT images which are observed both in human subjects and small animals.…”