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.Comment: 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