Lung Segmentation from Chest X-rays using Variational Data Imputation

Research output: Contribution to journalConference articleResearch


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.
Original languageEnglish
Number of pages7
Publication statusPublished - 20 May 2020
EventICML Workshop on Learning with Missing Values - Virtual
Duration: 17 Jul 2020 → …


WorkshopICML Workshop on Learning with Missing Values
Period17/07/2020 → …
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ID: 255780946