RadPleura: A Radiomics-Based Framework for Lung Pleura Classification in Histology Images from Interstitial Lung Diseases

Lung pleura is a reference structure for the identification of histological characteristics for the recognition of a pathological interstitial lung disease (ILD) pattern. When a pattern is found, it is important to know whether it is close to the pleura to determine its specific type and severity. This manual process is tedious and laborious for the pathologist. Automating this task is important for a complete computed-system-assisted ILD diagnostic process. We introduce "RadPleura" a framework for pleura classification of histopathological images using a radiomics-based approach. Our framework performs image pre-processing, region-of-interest segmentation, and extraction of a radiomic-signature suited for ILD classification. To evaluate a radiomic-signature, we classify it into pleura and non-pleura, using two classifiers, a Support Vector Machine (SVM) and Gradient-boosted Decision Tree (GBD). Our experiments are promising, producing F-scores of 92% (SVM) and 91% (GBD). We also created a dataset of lung histopathology images with respective ground truth for pleura classification. To the best of our knowledge, this study is the first published attempt to explore and develop a radiomic signature for pleura classification. The methods have been integrated into the RadPleura framework developed.