From Local to Global: A Holistic Lung Graph Model

Abstract

Lung image analysis is an essential part in the assessment of pulmonary diseases. Through visual inspection of CT scans, radiologists detect abnormal patterns in the lung parenchyma, aiming to establish a timely diagnosis and thus improving patient outcome. However, in a generalized disorder of the lungs, such as pulmonary hypertension, the changes in organ tissue can be elusive, requiring additional invasive studies to confirm the diagnosis. We present a graph model that quantifies lung texture in a holistic approach enhancing the analysis between pathologies with similar local changes. The approach extracts local state-of-the-art 3D texture descriptors from an automatically generated geometric parcellation of the lungs. The global texture distribution is encoded in a weighted graph that characterizes the correlations among neighboring organ regions. A data set of 125 patients with suspicion of having a pulmonary vascular pathology was used to evaluate our method. Three classes containing 47 pulmonary hypertension, 31 pulmonary embolism and 47 control cases were classified in a one vs. one setup. An area under the curve of up to 0.85 was obtained adding directionality to the edges of the graph architecture. The approach was able to identify diseased patients, and to distinguish pathologies with abnormal local and global blood perfusion defects.

A lung graph model for the classification of interstitial lung diseases on CT images

“…We modified the fully automated pipeline introduced by Dicente et al 10 to obtain specific graph models of the lungs for each disease. Our pipeline is composed of five steps, which includes (i) segmentation of the lung fields to create a mask of the lungs, (ii) subdivision of this mask into regions to create an atlas, (iii) extraction of the features for each region, (iv) creation of the distance matrix encoding the comparison between the regional features, and (v) creation of the graph model from a statistical test for all elements of the distance matrix to highlight the most relevant connections between regions.…”

Imaging-Based Deep Graph Neural Networks for Survival Analysis in Early Stage Lung Cancer Using CT: A Multicenter Study

“…This approach is unique in that it is able to elegantly incorporate connections from various features. In recent years, graph presentation has been widely used, for instance, social network analysis, language translation, and point cloud, also in the medical field such as vascular segmentation ( 14 ) and airway segmentation ( 15 ) due to the fact that some organs and systems within the human body are inherently based on graph or network structures (e.g., vascular structures such as retinal vessels) ( 16 , 17 ). Lungs also inherently have graph structures ( 18 ) if we regard every lung lobe as nodes connected by the airway which can be regarded as edges.…”