Cochlear Implant (CI) surgery treats severe hearing loss by inserting an electrode array into the cochlea to stimulate the auditory nerve. An important step in this procedure is mastoidectomy, which removes part of the mastoid region of the temporal bone to provide surgical access. Accurate mastoidectomy shape prediction from preoperative imaging improves pre-surgical planning, reduces risks, and enhances surgical outcomes. Despite its importance, there are limited deep-learning-based studies regarding this topic due to the challenges of acquiring ground-truth labels. We address this gap by investigating self-supervised and weakly-supervised learning models to predict the mastoidectomy region without human annotations. We propose a hybrid self-supervised and weakly-supervised learning framework to predict the mastoidectomy region directly from preoperative CT scans, where the mastoid remains intact. Our hybrid method achieves a mean Dice score of 0.72 when predicting the complex and boundary-less mastoidectomy shape, surpassing state-of-the-art approaches and demonstrating strong performance. The method provides groundwork for constructing 3D postmastoidectomy surfaces directly from the corresponding preoperative CT scans. To our knowledge, this is the first work that integrating self-supervised and weakly-supervised learning for mastoidectomy shape prediction, offering a robust and efficient solution for CI surgical planning while leveraging 3D T-distribution loss in weakly-supervised medical imaging.

翻译：人工耳蜗植入手术通过将电极阵列插入耳蜗以刺激听神经，从而治疗重度听力损失。该手术的关键步骤是乳突切除术，即切除颞骨乳突区部分骨质以建立手术通路。基于术前影像精确预测乳突切除术形状，能够优化术前规划、降低手术风险并提升手术效果。尽管该任务具有重要意义，但由于真实标注数据获取困难，目前基于深度学习的相关研究十分有限。本研究通过探索无需人工标注的自监督与弱监督学习模型，以填补这一空白。我们提出一种融合自监督与弱监督学习的混合框架，可直接从乳突结构完整的术前CT扫描中预测乳突切除区域。该混合方法在预测复杂且无明确边界的乳突切除术形状时，取得了0.72的平均Dice分数，超越了现有最优方法并展现出优异性能。本方法为直接从术前CT扫描构建三维乳突切除术后表面奠定了基础。据我们所知，这是首个将自监督与弱监督学习相结合用于乳突切除术形状预测的研究，通过引入三维T分布损失函数，为人工耳蜗手术规划提供了鲁棒高效的解决方案。