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.
Boccardi, F., Saalbach, A., Schulz, H., Salti, S., Sirazitdinov, I. (2024). Bottom-up Instance Segmentation of Catheters for Chest X-ray Images.
Bottom-up Instance Segmentation of Catheters for Chest X-ray Images
Samuele SaltiPenultimo
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2024
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.