  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.
