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Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio
Cardiothoratic ratio (CTR) estimated from chest radiographs is a marker
indicative of cardiomegaly, the presence of which is in the criteria for heart
failure diagnosis. Existing methods for automatic assessment of CTR are driven
by Deep Learning-based segmentation. However, these techniques produce only
point estimates of CTR but clinical decision making typically assumes the
uncertainty. In this paper, we propose a novel method for chest X-ray
segmentation and CTR assessment in an automatic manner. In contrast to the
previous art, we, for the first time, propose to estimate CTR with uncertainty
bounds. Our method is based on Deep Convolutional Neural Network with Feature
Pyramid Network (FPN) decoder. We propose two modifications of FPN: replace the
batch normalization with instance normalization and inject the dropout which
allows to obtain the Monte-Carlo estimates of the segmentation maps at test
time. Finally, using the predicted segmentation mask samples, we estimate CTR
with uncertainty. In our experiments we demonstrate that the proposed method
generalizes well to three different test sets. Finally, we make the annotations
produced by two radiologists for all our datasets publicly available.Comment: Roman Solovyev and Iaroslav Melekhov contributed equally. Timo
Lesonen and Elias Vaattovaara contributed equall