Abstract
Cranial implant design is a sophisticated time-intensive process performed by specialists uniquely for each patient using a set of standardized cranioplasty procedures. Automating the design of cranial implants for the ‘in-Operating-Room’ (in-OR) manufacturing pipeline is required to perform cranioplasty immediately after the primary surgery, thereby reducing the overall surgery time. In this manuscript, we propose an efficient cranial implant design workflow through a two-step approach in which we use two V-Net architectures, one to extract the region of cranial defect from the low-resolution skull and the other to reconstruct the cranial defect in the high-resolution skull. The extracted defective cranium is subtracted from the reconstructed cranium and is post-processed to obtain the fine implant. We further performed experiments to manufacture the cranial implant predicted by our proposed method through 3D printing, using titanium-aluminium (Ti6-Al4-V) alloy, a standard material used for medical prosthetics and implants. The proposed method is trained and evaluated on the data provided by the MICCAI 2021 AutoImplant Challenge. Our method performed well, giving a Dice Similarity Coefficient (DSC) of 0.90, border DSC of 0.95, and a 95-percentile of the Hausdorff Distance (HD95) of 2.02 mm over the test dataset (000.nrrd–109.nrrd).
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Ahn, S.H., Montero, M., Odell, D., Roundy, S., Wright, P.K.: Anisotropic material properties of fused deposition modeling abs. Rap. Prototyp. J. 8, 248–257 (2002)
Bayat, A., Shit, S., Kilian, A., Liechtenstein, J.T., Kirschke, J.S., Menze, B.H.: Cranial implant prediction using low-resolution 3D shape completion and high-resolution 2D refinement. In: Cranial Implant Design Challenge. pp. 77–84. Springer (2020)
von Campe, G., Pistracher, K.: Patient specific implants (PSI). In: Li, J., Egger, J. (eds.) AutoImplant 2020. LNCS, vol. 12439, pp. 1–9. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_1
Casamitjana, A., Catà, M., Sánchez, I., Combalia, M., Vilaplana, V.: Cascaded V-Net using ROI masks for brain tumor segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 381–391. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_33
Eder, M., Li, J., Egger, J.: Learning volumetric shape super-resolution for cranial implant design. In: Cranial Implant Design Challenge. pp. 104–113. Springer, Cham (2020)
Ellis, D.G., Aizenberg, M.R.: Deep learning using augmentation via registration: 1st place solution to the autoimplant 2020 challenge. In: Cranial Implant Design Challenge. pp. 47–55. Springer (2020)
Fedorov, A., Beichel, R., Kalpathy-Cramer, J., Finet, J., Fillion-Robin, J.C., Pujol, S., Bauer, C., Jennings, D., Fennessy, F., Sonka, M., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Mag. Resonan. Imag. 30(9), 1323–1341 (2012)
Jin, Y., Li, J., Egger, J.: High-resolution cranial implant prediction via patch-wise training. In: Cranial Implant Design Challenge. pp. 94–103. Springer (2020)
Kodym, O., Španěl, M., Herout, A.: Cranial defect reconstruction using cascaded cnn with alignment. In: Cranial Implant Design Challenge. pp. 56–64. Springer (2020)
Kruth, J.P., Froyen, L., Van Vaerenbergh, J., Mercelis, P., Rombouts, M., Lauwers, B.: Selective laser melting of iron-based powder. J. Mater. Process. Technol. 149(1–3), 616–622 (2004)
Lei, Y., Tian, S., He, X., Wang, T., Wang, B., Patel, P., Jani, A.B., Mao, H., Curran, W.J., Liu, T., et al.: Ultrasound prostate segmentation based on multidirectional deeply supervised v-net. Med. Phys. 46(7), 3194–3206 (2019)
Li, J., Egger, J.: Towards the Automatization of Cranial Implant Design in Cranioplasty. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0
Li, J., Pepe, A., Gsaxner, C., Campe, G., Egger, J.: A baseline approach for AutoImplant: the MICCAI 2020 cranial implant design challenge. In: Syeda-Mahmood, T., Drechsler, K., Greenspan, H., Madabhushi, A., Karargyris, A., Linguraru, M.G., Oyarzun Laura, C., Shekhar, R., Wesarg, S., González Ballester, M.Á., Erdt, M. (eds.) CLIP/ML-CDS -2020. LNCS, vol. 12445, pp. 75–84. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60946-7_8
Li, J., et al.: Autoimplant 2020-first miccai challenge on automatic cranial implant design. IEEE Trans. Med. Imag. 40(9), 2329–2342 (2021). https://doi.org/10.1109/TMI.2021.3077047
Mainprize, J.G., Fishman, Z., Hardisty, M.R.: Shape completion by U-Net: an approach to the AutoImplant MICCAI cranial implant design challenge. In: Li, J., Egger, J. (eds.) AutoImplant 2020. LNCS, vol. 12439, pp. 65–76. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_8
Matzkin, F., Newcombe, V., Glocker, B., Ferrante, E.: Cranial implant design via virtual craniectomy with shape priors. In: Cranial Implant Design Challenge, pp. 37–46. Springer, Cham (2020)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Nalepa, J., et al.: Data augmentation via image registration. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 4250–4254. IEEE (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
Shi, H., Chen, X.: Cranial implant design through multiaxial slice inpainting using deep learning. In: Li, J., Egger, J. (eds.) AutoImplant 2020. LNCS, vol. 12439, pp. 28–36. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_4
Wang, B., Liu, Z., Li, Y., Xiao, X., Zhang, R., Cao, Y., Cui, L., Zhang, P.: Cranial implant design using a deep learning method with anatomical regularization. In: Li, J., Egger, J. (eds.) AutoImplant 2020. LNCS, vol. 12439, pp. 85–93. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64327-0_10
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J., et al.: UNet++: a nested U-Net architecture for medical image segmentation. In: Stoyanov, D. (ed.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 3–11. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_1
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Pathak, S., Sindhura, C., Gorthi, R.K.S.S., Kiran, D.V., Gorthi, S. (2021). Cranial Implant Design Using V-Net Based Region of Interest Reconstruction. In: Li, J., Egger, J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty II. AutoImplant 2021. Lecture Notes in Computer Science(), vol 13123. Springer, Cham. https://doi.org/10.1007/978-3-030-92652-6_10
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DOI: https://doi.org/10.1007/978-3-030-92652-6_10
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