Video-Rate 4D OCT Segmentation Based on Motion-Aware Probabilistic A-Scan Sampling

Abstract

Recent advancements in robotic eye surgery and intraoperative 4D optical coherence tomography (iOCT) imaging could enable fully or partially autonomous robotic procedures and enhanced surgical visualization. A fundamental requirement for such applications is rapid semantic segmentation of intraoperative 4D OCT data, which is capable of acquiring volumes at video rate, to provide real-time three-dimensional scene perception. Significant advancements have been made in learning-based 2D and 3D OCT segmentation techniques, pushing the boundaries of accuracy and performance. However, despite these achievements, the computational demands of 2D and 3D convolutions make real-time intraoperative processing of 4D OCT infeasible, even with substantial computational resources.This work introduces a novel real-time iOCT volume segmentation methodology. The novelty consists of a dynamic motion-aware A-scan sampling strategy, followed by an efficient segmentation approach, guaranteeing both speed and accuracy of segmentation. Our A-scan-based processing network leverages a 1D convolution approach to resolve the complexities of multi-dimensional kernels and allow for maximum parallelization, resulting in significantly faster performance. We further show that OCT volume segmentation can be reconstructed from a sparse A-scan sampling strategy that prioritizes areas in which inter-volume motion was detected, and that even missing anatomical surface information below the surgical tools can be reconstructed. Our results show high segmentation performance in dynamic surgical environments and video-rate segmentation performance meeting the demanding processing requirements of 4D OCT and leading to substantial speed improvements over previous methods.