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Cardiothoratic ratio (CTR) estimated from chest radiographs is a markerindicative of cardiomegaly, the presence of which is in the criteria for heartfailure diagnosis. Existing methods for automatic assessment of CTR are drivenby Deep Learning-based segmentation. However, these techniques produce onlypoint estimates of CTR but clinical decision making typically assumes theuncertainty. In this paper, we propose a novel method for chest X-raysegmentation and CTR assessment in an automatic manner. In contrast to theprevious art, we, for the first time, propose to estimate CTR with uncertaintybounds. Our method is based on Deep Convolutional Neural Network with FeaturePyramid Network (FPN) decoder. We propose two modifications of FPN: replace thebatch normalization with instance normalization and inject the dropout whichallows to obtain the Monte-Carlo estimates of the segmentation maps at testtime. Finally, using the predicted segmentation mask samples, we estimate CTRwith uncertainty. In our experiments we demonstrate that the proposed methodgeneralizes well to three different test sets. Finally, we make the annotationsproduced by two radiologists for all our datasets publicly available.

Bayesian Feature Pyramid Networks for Automatic Multi-Label Segmentation of Chest X-rays and Assessment of Cardio-Thoratic Ratio