M&M: Unsupervised Mamba-based Mastoidectomy for Cochlear Implant Surgery with Noisy Data (2407.15787v3)
Abstract: Cochlear Implant (CI) procedures involve inserting an array of electrodes into the cochlea located inside the inner ear. Mastoidectomy is a surgical procedure that uses a high-speed drill to remove part of the mastoid region of the temporal bone, providing safe access to the cochlea through the middle and inner ear. We aim to develop an intraoperative navigation system that registers plans created using 3D preoperative Computerized Tomography (CT) volumes with the 2D surgical microscope view. Herein, we propose a method to synthesize the mastoidectomy volume using only the preoperative CT scan, where the mastoid is intact. We introduce an unsupervised learning framework designed to synthesize mastoidectomy. For model training purposes, this method uses postoperative CT scans to avoid manual data cleaning or labeling, even when the region removed during mastoidectomy is visible but affected by metal artifacts, low signal-to-noise ratio, or electrode wiring. Our approach estimates mastoidectomy regions with a mean dice score of 70.0%. This approach represents a major step forward for CI intraoperative navigation by predicting realistic mastoidectomy-removed regions in preoperative planning that can be used to register the pre-surgery plan to intraoperative microscopy.
- Labadie, R. and Noble, J., “Preliminary results with image-guided cochlear implant insertion techniques,” Otol Neurotol 39, 922–928 (Aug 2018).
- Dillon, N. P., Balachandran, R., Fitzpatrick, J., Siebold, M., Labadie, R., Wanna, G., Withrow, T., and Webster, R., “A compact, bone-attached robot for mastoidectomy.,” Journal of medical devices 9 3, 0310031–310037 (2015).
- You, C., Li, G., Zhang, Y., Zhang, X., Shan, H., Li, M., Ju, S., Zhao, Z., Zhang, Z., Cong, W., et al., “Ct super-resolution gan constrained by the identical, residual, and cycle learning ensemble (gan-circle),” IEEE transactions on medical imaging 39(1), 188–203 (2019).
- Arjovsky, M., Chintala, S., and Bottou, L., “Wasserstein generative adversarial networks,” in [Proceedings of the 34th International Conference on Machine Learning ], Precup, D. and Teh, Y. W., eds., Proceedings of Machine Learning Research 70, 214–223, PMLR (06–11 Aug 2017).
- Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A., “Image-to-image translation with conditional adversarial networks,” CVPR (2017).
- Dhariwal, P. and Nichol, A., “Diffusion models beat gans on image synthesis,” CoRR abs/2105.05233 (2021).
- Kazerouni, A., Aghdam, E. K., Heidari, M., Azad, R., Fayyaz, M., Hacihaliloglu, I., and Merhof, D., “Diffusion models in medical imaging: A comprehensive survey,” Medical Image Analysis 88, 102846 (2023).
- Xing, Z., Ye, T., Yang, Y., Liu, G., and Zhu, L., “Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation,” (2024).
- Wang, H., Guo, S., Ye, J., Deng, Z., Cheng, J., Li, T., Chen, J., Su, Y., Huang, Z., Shen, Y., Fu, B., Zhang, S., He, J., and Qiao, Y., “Sam-med3d,” (2023).
- Zhang, Y. and Noble, J. H., “Self-supervised registration and segmentation on ossicles with a single ground truth label,” in [Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling ], Linte, C. A. and Siewerdsen, J. H., eds., 12466, 124660X, International Society for Optics and Photonics, SPIE (2023).
- Wang, Z., Simoncelli, E., and Bovik, A., “Multiscale structural similarity for image quality assessment,” in [The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 ], Conference Record of the Asilomar Conference on Signals, Systems and Computers 2, 1398 – 1402 Vol.2 (12 2003).
- Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E., “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing 13(4), 600–612 (2004).
- Avants, B. B., Epstein, C. L., Grossman, M., and Gee, J. C., “Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain,” Medical Image Analysis 12(1), 26–41 (2008).
- Lorensen, W. E. and Cline, H. E., “Marching cubes: A high resolution 3d surface construction algorithm,” SIGGRAPH Comput. Graph. 21, 163–169 (aug 1987).
- Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., and Liang, J., “Unet++: Redesigning skip connections to exploit multiscale features in image segmentation,” IEEE Transactions on Medical Imaging (2019).