Lung Segmentation from Chest X-rays using Variational Data Imputation
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Lung Segmentation from Chest X-rays using Variational Data Imputation. / Selvan, Raghavendra; Dam, Erik B.; Rischel, Sofus; Sheng, Kaining; Nielsen, Mads; Pai, Akshay.
In: OpenReview.net, 20.05.2020.Research output: Contribution to journal › Conference article › Research
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TY - GEN
T1 - Lung Segmentation from Chest X-rays using Variational Data Imputation
AU - Selvan, Raghavendra
AU - Dam, Erik B.
AU - Rischel, Sofus
AU - Sheng, Kaining
AU - Nielsen, Mads
AU - Pai, Akshay
N1 - Source code, training data and the trained models are available here: https://github.com/raghavian/lungVAE/
PY - 2020/5/20
Y1 - 2020/5/20
N2 - 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.
AB - 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.
KW - eess.IV
KW - cs.CV
KW - cs.LG
KW - stat.ML
UR - https://openreview.net/forum?id=dlzQM28tq2W&
M3 - Conference article
JO - OpenReview.net
JF - OpenReview.net
T2 - ICML Workshop on Learning with Missing Values
Y2 - 17 July 2020
ER -
ID: 255780946