Pulmonary opacification is the inflammation in the lungs caused by many respiratory ailments, including the novel corona virus disease 2019 (COVID-19). Chest X-rays (CXRs) with such opacifications render regions of lungs imperceptible, making it difficult to perform automated image analysis on them. In this work, we focus on segmenting lungs from such abnormal CXRs as part of a pipeline aimed at automated risk scoring of COVID-19 from CXRs. We treat the high opacity regions as missing data and present a modified CNN-based image segmentation network that utilizes a deep generative model for data imputation. We train this model on normal CXRs with extensive data augmentation and demonstrate the usefulness of this model to extend to cases with extreme abnormalities.
@article{arxiv.2005.10052,
title = {Lung Segmentation from Chest X-rays using Variational Data Imputation},
author = {Raghavendra Selvan and Erik B. Dam and Nicki S. Detlefsen and Sofus Rischel and Kaining Sheng and Mads Nielsen and Akshay Pai},
journal= {arXiv preprint arXiv:2005.10052},
year = {2020}
}
Comments
Accepted to be presented at the first Workshop on the Art of Learning with Missing Values (Artemiss) hosted by the 37th International Conference on Machine Learning (ICML). Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE/