Bottom-Up Instance Segmentation of Catheters for Chest X-Rays
Francesca Boccardi Axel Saalbach Heinrich Schulz Samuele Salti Ilyas Sirazitdinov
Chest X-ray (CXR) is frequently employed in emergency departments and
intensive care units to verify the proper placement of central lines and tubes
and to rule out related complications. The automation of the X-ray reading
process can be a valuable support tool for non-specialist technicians and
minimize reporting delays due to non-availability of experts. While existing
solutions for automated catheter segmentation and malposition detection show
promising results, the disentanglement of individual catheters remains an open
challenge, especially in complex cases where multiple devices appear
superimposed in the X-ray projection. Moreover, conventional top-down instance
segmentation methods are ineffective on such thin and long devices, that often
extend through the entire image. In this paper, we propose a deep learning
approach based on associative embeddings for catheter instance segmentation,
able to overcome those limitations and effectively handle device intersections.