论文标题
基于对i3DUS数据的分析以进行神经外科诊断支持的组织表征
Tissue characterization based on the analysis on i3DUS data for diagnosis support in neurosurgery
论文作者
论文摘要
大脑转移使术前MRI导航高度不准确，因此术中的方式在手术剧院中采用。由于具有出色的经济和可移植性，因此在英国伦敦帝国伦敦帝国学院的Charing Cross医院的合作医院使用超声成像。但是，发现超声图像上的术中诊断即使对于经验丰富的临床专家，也不是一致的。因此，需要设计计算机辅助诊断系统，以提供强大的第二意见来帮助外科医生。提议的CAD系统基于“具有不对称损耗函数的混合注意RES-U-NET”，可以直接在像素级的分类中与地面真相进行比较，在所有评估中，它也优于像素级的分类（例如，U-U-NET，FCN）。
Brain shift makes the pre-operative MRI navigation highly inaccurate hence the intraoperative modalities are adopted in surgical theatre. Due to the excellent economic and portability merits, the Ultrasound imaging is used at our collaborating hospital, Charing Cross Hospital, Imperial College London, UK. However, it is found that intraoperative diagnosis on Ultrasound images is not straightforward and consistent, even for very experienced clinical experts. Hence, there is a demand to design a Computer-aided-diagnosis system to provide a robust second opinion to help the surgeons. The proposed CAD system based on "Mixed-Attention Res-U-net with asymmetric loss function" achieves the state-of-the-art results comparing to the ground truth by classification at pixel-level directly, it also outperforms all the current main stream pixel-level classification methods (e.g. U-net, FCN) in all the evaluation metrices.
