Initial Study On Improving Segmentation By Combining Preoperative CT And Intraoperative CBCT Using Synthetic Data

TL;DR

This study explores combining preoperative CT with intraoperative CBCT using synthetic data to enhance segmentation accuracy in computer-assisted interventions, addressing challenges posed by CBCT artifacts.

Contribution

It introduces a multimodal learning approach that fuses roughly aligned CT and CBCT scans, demonstrating improved segmentation performance with synthetic data.

Findings

Fusion improves segmentation in 18 out of 20 cases.

Synthetic data effectively simulates real CBCT for training.

Multimodal approach enhances image analysis accuracy.

Abstract

Computer-Assisted Interventions enable clinicians to perform precise, minimally invasive procedures, often relying on advanced imaging methods. Cone-beam computed tomography (CBCT) can be used to facilitate computer-assisted interventions, despite often suffering from artifacts that pose challenges for accurate interpretation. While the degraded image quality can affect image analysis, the availability of high quality, preoperative scans offers potential for improvements. Here we consider a setting where preoperative CT and intraoperative CBCT scans are available, however, the alignment (registration) between the scans is imperfect to simulate a real world scenario. We propose a multimodal learning method that fuses roughly aligned CBCT and CT scans and investigate the effect on segmentation performance. For this experiment we use synthetically generated data containing real CT and synthetic CBCT volumes with corresponding voxel annotations. We show that this fusion setup improves segmentation performance in $18$ out of $20$ investigated setups.