Bottom-up Instance Segmentation of Catheters for Chest X-ray Images

Francesca Boccardi, Axel Saalbach, Heinrich Schulz, Samuele Salti, Ilyas Sirazitdinov

Keywords: chest X-ray, instance segmentation, catheters, tubes, CVC, SWG

Abstract:

Chest X-ray (CXR) is frequently used 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.
In this paper, we propose a deep learning approach based on associative embeddings for catheter instance segmentation, able to effectively handle device intersections.

Submission Number: 121