Self-supervised Mamba-based Mastoidectomy Shape Prediction for Cochlear Implant Surgery

TL;DR

This paper introduces a self-supervised Mamba-based deep learning method to predict mastoidectomy shapes from preoperative CT scans, aiding cochlear implant surgery planning by reconstructing post-mastoidectomy anatomy.

Contribution

It presents a novel self-supervised framework that uses postoperative CT scans for training, enabling accurate mastoidectomy shape prediction without manual labeling.

Findings

Achieved a mean Dice score of 0.70 in shape estimation.

Effectively handles metal artifacts and low signal-to-noise ratio.

Supports improved surgical planning and intraoperative guidance.

Abstract

Cochlear Implant (CI) procedures require the insertion of an electrode array into the cochlea within the inner ear. To achieve this, mastoidectomy, a surgical procedure involving the removal of part of the mastoid region of the temporal bone using a high-speed drill provides safe access to the cochlea through the middle and inner ear. In this paper, we propose a novel Mamba-based method to synthesize the mastoidectomy volume using only preoperative Computed Tomography (CT) scans, where the mastoid remains intact. Our approach introduces a self-supervised learning framework designed to predict the mastoidectomy shape and reconstruct a 3D post-mastoidectomy surface directly from preoperative CT scans. This reconstruction aligns with intraoperative microscope views, enabling various downstream surgical applications. For training, we leverage postoperative CT scans to bypass manual data cleaning and labeling, even when the region removed during mastoidectomy is affected by challenges such as metal artifacts, low signal-to-noise ratio, or electrode wiring. Our method achieves a mean Dice score of 0.70 in estimating mastoidectomy regions, demonstrating its effectiveness for accurate and efficient surgical preoperative planning.