Abstract
Manual segmentation of the Glioblastoma is a challenging task for the radiologists, essential for treatment planning. In recent years deep convolutional neural networks have been shown to perform exceptionally well, in particular the winner of the BraTS challenge 2019 uses 3D U-net architecture in combination with variational autoencoder, using Dice overlap measure as a cost function. In this work we are proposing a loss function that approximates Hausdorff Distance metric that is used to evaluate performance of different segmentation in the hopes that it will allow achieving better performance of the segmentation on new data.
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References
Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2020. CA Canc. J. Clin. 70(1), 7–30 (2020). https://doi.org/10.3322/caac.21590. Epub 2020 Jan 8 PMID: 31912902
Ostrom, Q.T., Gittleman, H., Farah, P., Ondracek, A., Chen, Y., Wolinsky, Y., et al.: CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2006–2010. Neuro. Oncol. 15 Suppl:2ii–56 (2013)
Baid, U., et al.: The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification (2021). arXiv:2107.02314
Bakas, S., et al.: Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge (2018). arXiv:1811.02629
Tiwari, A., Srivastava, S., Pant, M.: Brain tumor segmentation and classification from magnetic resonance images: review of selected methods from 2014 to 2019. Pattern Recogn. Lett. 131, 244–260 (2020). ISSN 0167-8655
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization arXiv:1810.11654
Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15, 29 (2015). https://doi.org/10.1186/s12880-015-0068-x
Karimi, D., Salcudean, S.E.: Reducing the hausdorff distance in medical image segmentation with convolutional neural networks. IEEE Trans. Med. Imaging, 39(2), 499–513 (2019). arXiv:1904.10030
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
Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization arXiv:1412.6980
Loshchilov, I., Hutter, F.: Decoupled Weight Decay Regularization (2017). arXiv:1711.05101
Scipy exact distance transform: https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.distance_transform_edt.html
Crum, W.R., Camara, O., Derek, L.G.H.: Generalized overlap measures for evaluation and validation in medical image analysis. IEEE Trans. Med. Imag. 25(11), 1451–1461 (2006)https://doi.org/10.1109/TMI.2006.880587
Ketkar, N.: Introduction to PyTorch. In: Deep Learning with Python, pp. 195–208. Apress, Berkeley, CA (2017). https://doi.org/10.1007/978-1-4842-2766-4_12
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015). https://doi.org/10.1109/TMI.2014.2377694
Bakas, S., et al.: Advancing The Cancer genome Atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 170117 (2017). https://doi.org/10.1038/sdata.2017.117
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Canc. Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
Bakas, S., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Canc. Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Acknowledgments
Data used in this publication were obtained as part of the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge project through Synapse ID (syn25829067).
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Fonov, V.S., Rosa-Neto, P., Collins, D.L. (2022). 3D MRI Brain Tumour Segmentation with Autoencoder Regularization and Hausdorff Distance Loss Function. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12962. Springer, Cham. https://doi.org/10.1007/978-3-031-08999-2_27
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