Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part VII
Medical Image Computing and Computer Assisted Intervention - MICCAI 2022 - 25th International Conference, Singapore, September 18-22, 2022, Proceedings, Part VII
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医学图像计算和计算机辅助干预 - MICCAI 2022 - 第 25 届国际会议，新加坡，2022 年 9 月 18-22 日，会议记录，第七部分
DOI：
10.1007/978-3-031-16449-1_16
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Xu C
中科院分区：
文献类型：
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作者：
Xu C
基于探针的激光共聚焦内窥镜(PCLE)允许在手术中对细胞形态进行可视化，以确定组织的特征。机器人操作pCLE探头可以将探头与组织的接触保持在微米的工作范围内，以实现捕获高质量显微信息所需的精度和稳定性。在本文中，我们提出了第一种方法，在机器人组织扫描过程中，自动回归pCLE探针与组织表面之间的距离。空间-频率特征耦合网络(SFFC-Net)通过融合空域和频域特征提取增强的数据表示来回归探头-组织距离。图像级监督以一种新的方式用于回归，使网络能够有效地学习pCLE图像的清晰度与其到组织表面的距离之间的关系。因此，设计了一种新的反馈训练(FT)模块来合成未见图像，以将反馈纳入到训练过程中。生成了第一个pCLE回归数据集(PRD)，其中包括具有相应探头-组织距离的性别活体图像。我们的性能评估验证了该网络的性能优于其他最先进的(SOTA)回归网络。
Probe-based confocal laser endomicroscopy (pCLE) allowsin-situvisualisation of cellular morphology for intraoperative tissue characterization. Robotic manipulation of the pCLE probe can maintain the probe-tissue contact within micrometre working range to achieve the precision and stability required to capture good quality microscopic information. In this paper, we propose the first approach to automatically regress the distance between a pCLE probe and the tissue surface during robotic tissue scanning. The Spatial-Frequency Feature Coupling network (SFFC-Net) was designed to regress probe-tissue distance by extracting an enhanced data representation based on the fusion of spatial and frequency domain features. Image-level supervision is used in a novel fashion in regression to enable the network to effectively learn the relationship between the sharpness of the pCLE image and its distance from the tissue surface. Consequently, a novel Feedback Training (FT) module has been designed to synthesise unseen images to incorporate feedback into the training process. The first pCLE regression dataset (PRD) was generated which includesex-vivoimages with corresponding probe-tissue distance. Our performance evaluation verifies that the proposed network outperforms other state-of-the-art (SOTA) regression networks.