Abstract
The premorbid geometry of the mandible is of significant relevance in jaw reconstructive surgeries and occasionally unknown to the surgical team. In this paper, an optimization framework is introduced to train deep models for completion (reconstruction) of the missing segments of the bone based on the remaining healthy structure. To leverage the contextual information of the surroundings of the dissected region, the voxel-weighted Dice loss is introduced. To address the non-deterministic nature of the shape completion problem, we leverage a weighted multi-target probabilistic solution which is an extension to the conditional variational autoencoder (CVAE). This approach considers multiple targets as acceptable reconstructions, each weighted according to their conformity with the original shape. We quantify the performance gain of the proposed method against similar algorithms, including CVAE, where we report statistically significant improvements in both deterministic and probabilistic paradigms. The probabilistic model is also evaluated on its ability to generate anatomically relevant variations for the missing bone. As a unique aspect of this work, the model is tested on real surgical cases where the clinical relevancy of its reconstructions and their compliance with surgeon’s virtual plan are demonstrated as necessary steps towards clinical adoption.
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References
Abdi, A.H., et al.: AnatomyGen: deep anatomy generation from dense representation with applications in mandible synthesis. Technical report (2019)
Achlioptas, P., et al.: Representation learning and adversarial generation of 3D point clouds. 2(3), 4 arXiv preprint arXiv:1707.02392 (2017)
Brinkley, J.F., et al.: The FaceBase consortium: a comprehensive resource for craniofacial researchers. Development 143(14), 2677–2688 (2016). www.facebase.org
Hidalgo, D.A.: Fibula free flap: a new method of mandible reconstruction. Plast. Reconstr. Surg. 84(1), 71–79 (1989)
Milletari, F., et al.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV). IEEE (2016)
Nash, C., Williams, C.K.I.: The shape variational autoencoder: a deep generative model of part-segmented 3D objects. Comput. Graph. Forum 36(5), 1–12 (2017)
Ravikumar, N., Gooya, A., Çimen, S., Frangi, A.F., Taylor, Z.A.: A multi-resolution T-mixture model approach to robust group-wise alignment of shapes. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 142–149. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46726-9_17
Siegel, R.L., et al.: Cancer statistics. CA Cancer J. Clin. 67(1), 7–30 (2017)
Sohn, K., et al.: Learning structured output representation using deep conditional generative models. In: Cortes, C., et al. (ed.) Advances in Neural Information Processing Systems, vol. 28, pp. 3483–3491. Curran Associates, Inc. (2015)
Stranix, J.T., et al.: A virtual surgical planning algorithm for delayed maxillomandibular reconstruction. Plast. Reconstr. Surg. 143(4), 1197–1206 (2019)
Wu, J., et al.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems, pp. 82–90 (2016)
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Abdi, A.H., Pesteie, M., Prisman, E., Abolmaesumi, P., Fels, S. (2019). Variational Shape Completion for Virtual Planning of Jaw Reconstructive Surgery. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11768. Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_26
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DOI: https://doi.org/10.1007/978-3-030-32254-0_26
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