Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via
  Volumetric Pseudo-Labeling
Constantin Seibold Alexander Jaus Matthias A. Fink Moon Kim Simon Reiß Ken Herrmann Jens Kleesiek Rainer Stiefelhagen
Purpose: Interpreting chest radiographs (CXR) remains challenging due to the
ambiguity of overlapping structures such as the lungs, heart, and bones. To
address this issue, we propose a novel method for extracting fine-grained
anatomical structures in CXR using pseudo-labeling of three-dimensional
computed tomography (CT) scans.
  Methods: We created a large-scale dataset of 10,021 thoracic CTs with 157
labels and applied an ensemble of 3D anatomy segmentation models to extract
anatomical pseudo-labels. These labels were projected onto a two-dimensional
plane, similar to the CXR, allowing the training of detailed semantic
segmentation models for CXR without any manual annotation effort.
  Results: Our resulting segmentation models demonstrated remarkable
performance on CXR, with a high average model-annotator agreement between two
radiologists with mIoU scores of 0.93 and 0.85 for frontal and lateral anatomy,
while inter-annotator agreement remained at 0.95 and 0.83 mIoU. Our anatomical
segmentations allowed for the accurate extraction of relevant explainable
medical features such as the cardio-thoracic-ratio.
  Conclusion: Our method of volumetric pseudo-labeling paired with CT
projection offers a promising approach for detailed anatomical segmentation of
CXR with a high agreement with human annotators. This technique may have
important clinical implications, particularly in the analysis of various
thoracic pathologies.