Abstract
We present a new method for end-to-end automatic volumetric segmentation of fetal structures in MRI scans with deep learning networks trained with very few annotated scans. It consists of three main stages: 1) two-step automatic structure segmentation with custom 3D U-Nets; 2) segmentation error estimation, and; 3) segmentation error correction. The automatic structure segmentation stage first computes a region of interest (ROI) on a downscaled scan and then computes a final segmentation on the cropped ROI. The segmentation error estimation stage uses prediction-time augmentations of the input scan to compute multiple segmentations and estimate the segmentation uncertainty for individual slices and for the entire scan. The segmentation error correction stage then uses these estimations to locate the most error-prone slices and to correct the segmentations in those slices based on validated adjacent slices. Experimental results of our methods on fetal body (63 cases, 9 for training, 55 for testing) and fetal brain MRI scans (35 cases, 6 for training, 29 for testing) yield a mean Dice coefficient of 0.96 for both, and a mean Average Symmetric Surface Distance of 0.74 mm and 0.19 mm, respectively, below the observer delineation variability.
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References
Litjens, G., et al.: A survey of deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
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: Proceedings of the 4th IEEE International Conference on 3D Vision (2016)
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 287–297. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_25
Karimi, D., Samei, G., Shao, Y., Salcudean, S.: A novel deep learning-based method for prostate segmentation in T2-weighted magnetic resonance imaging. arXiv:1901.09462 (2019)
Salehi, S.S.M., et al.: Real-time automatic fetal brain extraction in fetal MRI by deep learning. In: Proceedings of the IEEE 15th International Symposium on Biomedical Imaging – ISBI 2018, pp. 720–724 (2018)
Gibson, E., et al.: NiftyNet: a deep-learning platform for medical imaging. Comput. Methods Progr. Biomed. 158, 113–122 (2018)
Gholipour, A., et al.: A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth. Sci. Rep. 7(1), 1–13 (2017)
Fetit, A.E., et al.: A deep learning approach to segmentation of the developing cortex in fetal brain MRI with minimal manual labeling. In: Medical Imaging with Deep Learning (2020)
Veeraraghavan, H., Miller, J.V.: Active learning guided interactions for consistent image segmentation with reduced user interactions. In: Proceedings of the IEEE International Symposium on Biomed Imaging (2011)
Lee, N., Caban, J., Ebadollahi, S., Laine, A.: Interactive segmentation in multimodal medical imagery using a Bayesian transductive learning approach. In: Proceedings of the IEEE International Symposium on Biomedical Imaging (2009)
Wang, G., Li, W.: Interactive medical image segmentation using deep learning with image-specific fine tuning. IEEE Trans. Med. Imaging 37, 1562–1573 (2018)
Braginsky, M., Joskowicz, L.: Interactive segmentation of structures with real-time fine-tuning of a fully convolutional neural network. M.Sc. thesis, The Hebrew University of Jerusalem (2019)
Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in Neural Information Processing Systems (2017)
Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70 (2017)
Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., Vercauteren, T.: Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 338, 34–45 (2019)
Loquercio, A., Segu, M., Scaramuzza, D.: A general framework for uncertainty estimation in Deep Learning. arXiv preprint arXiv:1907.06890 (2019)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38
Tompson, J., Goroshin, R., Jain, A., Lecun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Lin, H., et al.: Deep learning for low-field to high-field MR: image quality transfer with probabilistic decimation simulator. In: Knoll, F., Maier, A., Rueckert, D., Ye, J.C. (eds.) MLMIR 2019. LNCS, vol. 11905, pp. 58–70. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33843-5_6
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Dudovitch, G., Link-Sourani, D., Ben Sira, L., Miller, E., Ben Bashat, D., Joskowicz, L. (2020). Deep Learning Automatic Fetal Structures Segmentation in MRI Scans with Few Annotated Datasets. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12266. Springer, Cham. https://doi.org/10.1007/978-3-030-59725-2_35
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