IEEE Trans Med Imaging. 2019 Apr;38(4):1026-1036. doi: 10.1109/TMI.2018.2876796. Epub 2018 Oct 18.
Image guidance improves tissue sampling during biopsy by allowing the physician to visualize the tip and trajectory of the biopsy needle relative to the target in MRI, CT, ultrasound, or other relevant imagery. This paper reports a system for fast automatic needle tip and trajectory localization and visualization in MRI that has been developed and tested in the context of an active clinical research program in prostate biopsy. To the best of our knowledge, this is the first reported system for this clinical application and also the first reported system that leverages deep neural networks for segmentation and localization of needles in MRI across biomedical applications. Needle tip and trajectory were annotated on 583 T2-weighted intra-procedural MRI scans acquired after needle insertion for 71 patients who underwent transperineal MRI-targeted biopsy procedure at our institution. The images were divided into two independent training-validation and test sets at the patient level. A deep 3-D fully convolutional neural network model was developed, trained, and deployed on these samples. The accuracy of the proposed method, as tested on previously unseen data, was 2.80-mm average in needle tip detection and 0.98° in needle trajectory angle. An observer study was designed in which independent annotations by a second observer, blinded to the original observer, were compared with the output of the proposed method. The resultant error was comparable to the measured inter-observer concordance, reinforcing the clinical acceptability of the proposed method. The proposed system has the potential for deployment in clinical routine.
图像引导通过使医生能够在 MRI、CT、超声或其他相关图像中可视化活检针的尖端和轨迹相对于目标的位置，从而提高活检时的组织采样质量。本文报告了一种用于在 MRI 中快速自动定位和可视化活检针尖端和轨迹的系统，该系统是在前列腺活检的活跃临床研究计划的背景下开发和测试的。据我们所知，这是第一个针对该临床应用的报告系统，也是第一个利用深度神经网络在跨生物医学应用中对 MRI 中的针进行分割和定位的报告系统。在我们机构进行经会阴 MRI 靶向活检后，对 71 名患者的 583 次术中 T2 加权 MRI 扫描进行了针尖端和轨迹标注。这些图像在患者水平上分为两个独立的训练-验证和测试集。开发、训练和部署了一个深度 3D 全卷积神经网络模型来处理这些样本。在对以前未见的数据进行测试时，该方法的准确性为 2.80 毫米，针尖端检测的平均精度为 0.98°，针轨迹角度的平均精度为 0.98°。设计了一项观察者研究，其中由第二位观察者进行独立标注，该观察者对原始观察者的标注情况不知情，并将其与所提出方法的输出进行比较。所得误差与测量的观察者间一致性相当，这增强了所提出方法的临床可接受性。该系统具有在临床常规中部署的潜力。
