Abstract
Automatic segmentation of medical images is a key step for diagnostic and interventional tasks. However, achieving this requires large amounts of annotated volumes, which can be tedious and time-consuming task for expert annotators. In this paper, we introduce DeepEdit, a deep learning-based method for volumetric medical image annotation, that allows automatic and semi-automatic segmentation, and click-based refinement. DeepEdit combines the power of two methods: a non-interactive (i.e. automatic segmentation using nnU-Net, UNET or UNETR) and an interactive segmentation method (i.e. DeepGrow), into a single deep learning model. It allows easy integration of uncertainty-based ranking strategies (i.e. aleatoric and epistemic uncertainty computation) and active learning. We propose and implement a method for training DeepEdit by using standard training combined with user interaction simulation. Once trained, DeepEdit allows clinicians to quickly segment their datasets by using the algorithm in auto segmentation mode or by providing clicks via a user interface (i.e. 3D Slicer, OHIF). We show the value of DeepEdit through evaluation on the PROSTATEx dataset for prostate/prostatic lesions and the Multi-Atlas Labeling Beyond the Cranial Vault (BTCV) dataset for abdominal CT segmentation, using state-of-the-art network architectures as baseline for comparison. DeepEdit could reduce the time and effort annotating 3D medical images compared to DeepGrow alone. Source code is available at https://github.com/Project-MONAI/MONAILabel.
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
References
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
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T., Ronneberger, O.: 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 424–432. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46723-8_49
Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation. In: 3DV (2016)
Isensee, F., Jaeger, P.F., Kohl, S.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Meth. 18, 203–211 (2020)
He, Y., Yang, D., Roth, H., Zhao, C., Xu, D.: DiNTS: differentiable neural network topology search for 3D medical image segmentation. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5837–5846 (2021)
Hatamizadeh, A., et al.: UNETR: transformers for 3D medical image segmentation. In: 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 1748–1758 (2022)
Antonelli, M., et al.: The medical segmentation decathlon. Nat. Commun. 13(1), 1–13 (2022)
Vaswani, A., et al.: Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS 2017, vol. 2017, pp. 6000–6010 (2017)
Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H.R., Xu, D.: Swin UNETR: swin transformers for semantic segmentation of brain tumors in MRI images. In: Crimi, A., Bakas, S. (eds.) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part I, pp. 272–284. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08999-2_22
Sakinis, T., et al.: Interactive segmentation of medical images through fully convolutional neural networks. arXiv preprint arXiv:1903.08205 (2019)
Zhao, F., Xie, X.: An overview of interactive medical image segmentation. Ann. Brit. Mach. Vis. Assoc. 2013(7), 1–22 (2013)
Shi, J., Malik, J.: Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 888–905 (2000)
Grady, L., Schiwietz, T., Aharon, S., Westermann, R.: Random walks for interactive organ segmentation in two and three dimensions: implementation and validation. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 773–780. Springer, Heidelberg (2005). https://doi.org/10.1007/11566489_95
Boykov, Y., Funka-Lea, G.: Graph cuts and efficient N-D image segmentation. Int. J. Comput. Vis. 70(2), 109–131 (2006)
Akkus, Z., et al.: Semi-automated segmentation of pre-operative low grade gliomas in magnetic resonance imaging. Cancer Imaging 15(12), 1–10 (2015)
Xu, N., Price, B., Cohen, S., Yang, J., Huang, T.: Deep interactive object selection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 373–381 (2016)
Agustsson, E., Uijlings, J.R., Ferrari, V.: Interactive full image segmentation by considering all regions jointly. In: 2019 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 11614–11623 (2019)
Wang, G., et al.: Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans. Med. Imaging 37(7), 1562–1573 (2018)
Wang, G., et al.: DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 41(7), 1559–1572 (2019)
Yushkevich, P.A., et al.: User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3), 1116–1128 (2006). https://doi.org/10.1016/j.neuroimage.2006.01.015
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1988)
Fedorov, A., et al.: 3D slicer as an image computing platform for the quantitative imaging network. Magn. Reson. Imaging 30, 1323–1341 (2012)
Nolden, M., et al.: The medical imaging interaction toolkit: challenges and advances: 10 years of open-source development. Int. J. Comput. Assist. Radiol. Surg. 8(4), 607–620 (2013)
Maninis, K.K., Caelles, S., Pont-Tuset, J., Van Gool, L.: Deep extreme cut: from extreme points to object segmentation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 616–625 (2018)
Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)
Osher, S., Sethian, J.A.: Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys. 79(1), 12–49 (1988)
MONAI Consortium: MONAI: Medical Open Network for AI, March 2020
Litjens, G., Debats, O., Barentsz, J., Karssemeijer, N., Huisman, H.: ProstateX Challenge data (2017)
Mehta, P., et al.: AutoProstate: towards automated reporting of prostate MRI for prostate cancer assessment using deep learning. Cancers 13(23), 6138 (2021)
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
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Diaz-Pinto, A. et al. (2022). DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical Images. In: Nguyen, H.V., Huang, S.X., Xue, Y. (eds) Data Augmentation, Labelling, and Imperfections. DALI 2022. Lecture Notes in Computer Science, vol 13567. Springer, Cham. https://doi.org/10.1007/978-3-031-17027-0_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-17027-0_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17026-3
Online ISBN: 978-3-031-17027-0
eBook Packages: Computer ScienceComputer Science (R0)
