嵌套ResNet：一种用于检测置入式伽马探针传感区域的基于视觉的方法
Nested ResNet: A Vision-Based Method for Detecting the Sensing Area of a Drop-in Gamma Probe
AI总结：
针对机器人辅助微创手术中置入式伽马探针缺乏视觉反馈、传感区域定位精度不足的问题，提出融合Nested ResNet主分支、深度估计与方向引导的三分支框架，在公开数据集上大幅降低2D和3D平均误差，提升手术定位可靠性。
摘要：
目的：置入式伽马探针广泛应用于机器人辅助微创手术（RAMIS）中的淋巴结检测。然而，这些设备仅提供信号强度的音频反馈，缺乏精确定位所需的视觉反馈。先前的研究尝试利用腹腔镜图像预测传感区域位置，但预测精度不尽如人意，基于深度学习的回归方法有待改进。方法：我们提出一种三分支深度学习框架来预测探针的传感区域。具体而言，我们将立体腹腔镜图像作为主分支的输入，并构建了Nested ResNet（嵌套残差网络）架构。该框架还结合了通过迁移学习实现的深度估计，以及通过探针轴采样实现的方向引导。各分支的融合特征提升了预测的精度。结果：我们的方法在一个公开数据集上进行了评估，表现出优于现有方法的性能。具体来说，我们的方法使2D平均误差降低了22.10%，3D平均误差降低了41.67%。此外，定性比较进一步证明了我们方法精度的提升。结论：经过广泛评估，我们的方案显著提高了传感区域预测的准确性和可靠性。这一进展为手术中使用置入式伽马探针提供了视觉反馈，为外科医生提供了更准确可靠的定位支持。
英文摘要：
Purpose: Drop-in gamma probes are widely used in robotic-assisted minimally invasive surgery (RAMIS) for lymph node detection. However, these devices only provide audio feedback on signal intensity, lacking the visual feedback necessary for precise localisation. Previous work attempted to predict the sensing area location using laparoscopic images, but the prediction accuracy was unsatisfactory. Improvements are needed in the deep learning-based regression approach. Methods: We introduce a three-branch deep learning framework to predict the sensing area of the probe. Specifically, we utilise the stereo laparoscopic images as input for the main branch and develop a Nested ResNet architecture. The framework also incorporates depth estimation via transfer learning and orientation guidance through probe axis sampling. The combined features from each branch enhanced the accuracy of the prediction. Results: Our approach has been evaluated on a publicly available dataset, demonstrating superior performance over previous methods. In particular, our method resulted in a 22.10\% decrease in 2D mean error and a 41.67\% reduction in 3D mean error. Additionally, qualitative comparisons further demonstrated the improved precision of our approach. Conclusion: With extensive evaluation, our solution significantly enhances the accuracy and reliability of sensing area predictions. This advancement enables visual feedback during the use of the drop-in gamma probe in surgery, providing surgeons with more accurate and reliable localisation.}
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。
