Many interventional procedures require the precise placement of needles or therapy applicators (tools) to correctly achieve planned targets for optimal diagnosis or treatment of cancer, typically leveraging the temporal resolution of ultrasound (US) to provide real‐time feedback. Identifying tools in two‐dimensional (2D) images can often be time‐consuming with the precise position difficult to distinguish. We have developed and implemented a deep learning method to segment tools in 2D US images in near real‐time for multiple anatomical sites, despite the widely varying appearances across interventional applications.

中文翻译：

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二维超声图像中一般介入工具的深度学习细分。

许多介入程序需要精确地放置针头或治疗涂药器（工具）以正确实现计划的目标，以最佳地诊断或治疗癌症，通常利用超声的时间分辨率（US）来提供实时反馈。在二维（2D）图像中识别工具通常很耗时，而精确的位置却难以区分。我们已经开发并实施了一种深度学习方法，以在多个解剖部位几乎实时地在2D美国图像中分割工具，尽管各种介入应用程序的外观差异很大。