Bottom-Up Instance Segmentation of Catheters for Chest X-Rays
This addresses the challenge of automating catheter segmentation for non-specialist technicians in emergency and intensive care settings, though it appears incremental as it builds on existing methods for a specific bottleneck.
The paper tackles the problem of disentangling individual catheters in chest X-rays, especially when multiple devices are superimposed, by proposing a deep learning approach based on associative embeddings for instance segmentation, which effectively handles device intersections.
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.