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

## TL;DR

This paper introduces a Bayesian feature pyramid network approach for automatic chest X-ray segmentation and cardiothoracic ratio estimation, providing uncertainty bounds to improve clinical decision-making.

## Contribution

It is the first to estimate CTR with uncertainty bounds using a modified FPN with dropout, enhancing the reliability of automated assessments.

## Key findings

- The method generalizes well across three test sets.
- Uncertainty estimates improve clinical decision support.
- Publicly available annotations facilitate further research.

## Abstract

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.