用于胸部X光片的导管自底向上实例分割
Bottom-Up Instance Segmentation of Catheters for Chest X-Rays
AI总结：
针对胸部X光片中多导管重叠导致个体难以区分的问题，提出基于关联嵌入的自底向上深度学习实例分割方法，以有效处理细长导管的交叉。
摘要：
胸部X光片（CXR）常被用于急诊科和重症监护室，以确认中心静脉导管和各类管路的位置是否正确，并排除相关并发症。X光阅片流程的自动化可成为非专科技术人员的宝贵辅助工具，并最大限度减少因专家不在场而导致的报告延迟。尽管现有的自动导管分割和位置异常检测解决方案已展现出良好效果，但如何区分各根导管仍是一项尚未解决的挑战，尤其是在多个器械于X光投影中相互重叠的复杂病例中。此外，传统的自顶向下实例分割方法对此类细长器械并不有效，因为它们往往延伸贯穿整幅图像。本文提出一种基于关联嵌入的深度学习方法，用于导管实例分割，能够克服上述局限并有效处理器械交叉情况。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
