Purpose: Middle ear infection is the most prevalent inflammatory disease, especially among the pediatric population. Current diagnostic methods are subjective and depend on visual cues from an otoscope, which is limited for otologists to identify pathology. To address this shortcoming, endoscopic optical coherence tomography (OCT) provides both morphological and functional in-vivo measurements of the middle ear. However, due to the shadow of prior structures, interpretation of OCT images is challenging and time-consuming. To facilitate fast diagnosis and measurement, improvement in the readability of OCT data is achieved by merging morphological knowledge from ex-vivo middle ear models with OCT volumetric data, so that OCT applications can be further promoted in daily clinical settings. Methods: We propose C2P-Net: a two-staged non-rigid registration pipeline for complete to partial point clouds, which are sampled from ex-vivo and in-vivo OCT models, respectively. To overcome the lack of labeled training data, a fast and effective generation pipeline in Blender3D is designed to simulate middle ear shapes and extract in-vivo noisy and partial point clouds. Results: We evaluate the performance of C2P-Net through experiments on both synthetic and real OCT datasets. The results demonstrate that C2P-Net is generalized to unseen middle ear point clouds and capable of handling realistic noise and incompleteness in synthetic and real OCT data. Conclusion: In this work, we aim to enable diagnosis of middle ear structures with the assistance of OCT images. We propose C2P-Net: a two-staged non-rigid registration pipeline for point clouds to support the interpretation of in-vivo noisy and partial OCT images for the first time. Code is available at: https://gitlab.com/nct\_tso\_public/c2p-net.

翻译：目的：中耳感染是最常见的炎症性疾病，尤其在儿科人群中高发。目前的诊断方法具有主观性，仅依赖耳镜的视觉线索，这限制了耳科医生识别病理的能力。为解决这一不足，内窥镜光学相干断层扫描（OCT）可提供中耳的形态与功能性体内测量。然而，由于前方结构的阴影遮挡，OCT图像的判读具有挑战性且耗时。为促进快速诊断与测量，通过将离体中耳模型的形态学知识与OCT体积数据融合，提升了OCT数据的可读性，从而推动OCT在临床日常中的应用。方法：我们提出C2P-Net：一种面向完整到部分点云的两阶段非刚性配准管线，分别从离体和体内OCT模型中采样点云。为克服标注训练数据不足的问题，我们在Blender3D中设计了一种快速高效的数据生成管线，用于模拟中耳形状并提取含噪声的体内部分点云。结果：我们通过在合成数据集与真实OCT数据集上的实验评估了C2P-Net的性能。结果表明，C2P-Net能够泛化到未见过的中耳点云，并具备处理合成及真实OCT数据中真实噪声与不完整性的能力。结论：本工作旨在借助OCT图像辅助中耳结构诊断。我们首次提出C2P-Net：一种支持体内含噪声部分OCT图像判读的点云两阶段非刚性配准管线。代码开源地址：https://gitlab.com/nct_tso_public/c2p-net。