Abstract
Despite recent successes, the advances in Deep Learning have not yet been fully translated to Computer Assisted Intervention (CAI) problems such as pose estimation of surgical instruments. Currently, neural architectures for classification and segmentation tasks are adopted ignoring significant discrepancies between CAI and these tasks. We propose an automatic framework (AutoSNAP) for instrument pose estimation problems, which discovers and learns architectures for neural networks. We introduce 1) an efficient testing environment for pose estimation, 2) a powerful architecture representation based on novel Symbolic Neural Architecture Patterns (SNAPs), and 3) an optimization of the architecture using an efficient search scheme. Using AutoSNAP, we discover an improved architecture (SNAPNet) which outperforms both the hand-engineered i3PosNet and the state-of-the-art architecture search method DARTS.
D. Kügler and M. Uecker—Equal contribution.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Our code is available at https://github.com/MECLabTUDA/AutoSNAP.
- 2.
We provide additional diagrams of the architectures in the Supplementary Materials.
References
Bae, W., Lee, S., Lee, Y., Park, B., Chung, M., Jung, K.-H.: Resource optimized neural architecture search for 3D medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11765, pp. 228–236. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32245-8_26
Balakrishnan, G., Zhao, A., Sabuncu, M.R., Guttag, J., Dalca, A.V.: VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging 38(8), 1788–1800 (2019)
Dong, N., Xu, M., Liang, X., Jiang, Y., Dai, W., Xing, E.: Neural architecture search for adversarial medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11769, pp. 828–836. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32226-7_92
Elsken, T., Metzen, J.H., Hutter, F.: Neural architecture search: a survey 20, 1–21 (2019). http://jmlr.org/papers/v20/18-598.html
Hajj, H.A., et al.: CATARACTS: challenge on automatic tool annotation for cataract surgery. Med. IA 52, 24–41 (2019)
Kim, S., et al.: Scalable neural architecture search for 3D medical image segmentation. In: Shen, D., et al. (eds.) MICCAI 2019. LNCS, vol. 11766, pp. 220–228. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32248-9_25
Kügler, D., et al.: i3posnet: instrument pose estimation from x-ray in temporal bone surgery. Int. J. Comput. Assist. Radiol. Surg. 15(7), 1137–1145 (2020). https://doi.org/10.1007/s11548-020-02157-4
Liu, H., Simonyan, K., Yang, Y.: DARTS: differentiable architecture search. In: ICLR 2019 (2019). https://arxiv.org/pdf/1806.09055
Luo, R., Tian, F., Qin, T., Chen, E., Liu, T.Y.: Neural architecture optimization. In: Bengio, S., et al. (eds.) Advances in NeurIPS, vol. 31. Curran Associates, Inc. (2018)
Maier-Hein, L., et al.: Surgical data science for next-generation interventions. Nat. BioMed. Eng. 1(9), 691–696 (2017)
Miao, S., Wang, Z.J., Liao, R.: A CNN regression approach for real-time 2D/3D registration. IEEE Trans. Med. Imaging 35(5), 1352–1363 (2016)
Schipper, J., et al.: Navigation as a quality management tool in cochlear implant surgery. J. Laryngol. Otol. 118(10), 764–770 (2004)
Twinanda, A.P., Shehata, S., Mutter, D., Marescaux, J., de Mathelin, M., Padoy, N.: EndoNet: a deep architecture for recognition tasks on laparoscopic videos. IEEE Trans. Med. Imaging 36(1), 86–97 (2017)
Unberath, M., et al.: Enabling machine learning in x-ray-based procedures via realistic simulation of image formation. Int. J. Comput. Assist. Radiol. Surg. 14(9), 1517–1528 (2019). https://doi.org/10.1007/s11548-019-02011-2
Vercauteren, T., Unberath, M., Padoy, N., Navab, N.: CAI4CAI: the rise of contextual artificial intelligence in computer assisted interventions. Proc. IEEE 108(1), 198–214 (2020). https://doi.org/10.1109/JPROC.2019.2946993
Weng, Y., Zhou, T., Li, Y., Qiu, X.: NAS-Unet: neural architecture search for medical image segmentation. IEEE Access 7, 44247–44257 (2019)
Yu, Q., et al.: C2FNAS: coarse-to-fine neural architecture search for 3D medical image segmentation (2019). https://arxiv.org/pdf/1912.09628
Zhu, Z., Liu, C., Yang, D., Yuille, A., Xu, D.: V-NAS: neural architecture search for volumetric medical image segmentation. In: 2019 International Conference on 3D Vision, pp. 240–248. IEEE Computer Society, Conference Publishing Services, Los Alamitos (2019)
Zoph, B., Vasudevan, V., Shlens, J., Le, V.Q.: Learning transferable architectures for scalable image recognition. In: Brown, M.S., et al. (eds.) CVPR Proceedings (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Kügler, D., Uecker, M., Kuijper, A., Mukhopadhyay, A. (2020). AutoSNAP: Automatically Learning Neural Architectures for Instrument Pose Estimation. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham. https://doi.org/10.1007/978-3-030-59716-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-59716-0_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59715-3
Online ISBN: 978-3-030-59716-0
eBook Packages: Computer ScienceComputer Science (R0)
