Abstract
Accurate and reproducible segmentation of brain tumors from multi-modal magnetic resonance (MR) scans is a pivotal step in clinical practice, as MR imaging is the modality of choice in brain tumor diagnosis and assessment, and incorrectly delineated tumor areas may adversely affect the process of designing the treatment pathway. In this paper, we exploit an end-to-end 3D nnU-Net architecture for this task, and utilize an ensemble of five models using our custom stratification based on the distribution of the necrosis, enhancing tumor, and edema. To improve the segmentation, we benefit from the experience of a senior radiologist captured in a form of several post-processing routines. The experiments obtained for the BraTS’21 training and validation sets show that exploiting such expert knowledge can significantly improve the underlying models, delivering the average Dice score of 0.81977 (enhancing tumor), 0.87837 (tumor core), and 0.92723 (whole tumor). Finally, our algorithm allowed us to take the \(6^\mathrm{th}\) place (out of 1600 participants) in the BraTS’21 Challenge, with the average Dice score over the test data of 0.86317, 0.87987, and 0.92838 for the enhancing tumor, tumor core and whole tumor, respectively.
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Notes
- 1.
Note that increasing the number of training epochs would likely improve the capabilities of the model even further.
- 2.
Our team name is Future Processing Healthcare.
References
Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification (2021)
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Nat. Sci. Data 4, 1–13 (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. Cancer 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. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRR abs/1811.02629 (2018). http://arxiv.org/abs/1811.02629
Bontempi, D., Benini, S., Signoroni, A., Svanera, M., Muckli, L.: Cerebrum: a fast and fully-volumetric convolutional encoder-decoder for weakly-supervised segmentation of brain structures from out-of-the-scanner MRI. Med. Image Anal. 62, 101688 (2020)
Chander, A., Chatterjee, A., Siarry, P.: A new social and momentum component adaptive PSO algorithm for image segmentation. Expert Syst. Appl. 38(5), 4998–5004 (2011)
Estienne, T., et al.: Deep learning-based concurrent brain registration and tumor segmentation. Front. Comput. Neurosci. 14, 17 (2020)
Geremia, E., Clatz, O., Menze, B.H., Konukoglu, E., Criminisi, A., Ayache, N.: Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. Neuroimage 57(2), 378–390 (2011)
Ghafoorian, M., et al.: Transfer learning for domain adaptation in MRI: application in brain lesion segmentation. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 516–524. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_59
Isensee, F., Jaeger, P., Kohl, S., Petersen, J., Maier-Hein, K.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18, 1–9 (2021). https://doi.org/10.1038/s41592-020-01008-z
Isensee, F., Kickingereder, P., Wick, W., Bendszus, M., Maier-Hein, K.H.: No new-net. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 234–244. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_21
Ji, S., Wei, B., Yu, Z., Yang, G., Yin, Y.: A new multistage medical segmentation method based on superpixel and fuzzy clustering. Comput. Math. Methods Med. 2014, 747549:1–747549:13 (2014)
Kamnitsas, K., et al.: Ensembles of multiple models and architectures for robust brain tumour segmentation. In: Crimi, A., Bakas, S., Kuijf, H., Menze, B., Reyes, M. (eds.) BrainLes 2017. LNCS, vol. 10670, pp. 450–462. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-75238-9_38
Korfiatis, P., Kline, T.L., Erickson, B.J.: Automated segmentation of hyperintense regions in FLAIR MRI using deep learning. Tomography J. Imaging Res. 2(4), 334–340 (2016)
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
Moeskops, P., Viergever, M.A., Mendrik, A.M., de Vries, L.S., Benders, M.J.N.L., Isgum, I.: Automatic segmentation of MR brain images with a convolutional neural network. IEEE Trans. Med. Imaging 35(5), 1252–1261 (2016)
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Nalepa, J., Marcinkiewicz, M., Kawulok, M.: Data augmentation for brain-tumor segmentation: a review. Front. Comput. Neurosci. 13, 83 (2019)
Nalepa, J., et al.: Fully-automated deep learning-powered system for DCE-MRI analysis of brain tumors. Artif. Intell. Med. 102, 101769 (2020)
Ouchicha, C., Ammor, O., Meknassi, M.: Unsupervised brain tumor segmentation from magnetic resonance images. In: Proceedings of the IEEE WINCOM, pp. 1–5 (2019)
Pipitone, J., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101, 494–512 (2014)
Saha, S., Bandyopadhyay, S.: MRI brain image segmentation by fuzzy symmetry based genetic clustering technique. In: Proceedings of the IEEE CEC, pp. 4417–4424 (2007)
Simi, V., Joseph, J.: Segmentation of glioblastoma multiforme from MR images - a comprehensive review. Egyptian J. Radiol. Nuclear Med. 46(4), 1105–1110 (2015)
Verma, N., Cowperthwaite, M.C., Markey, M.K.: Superpixels in brain MR image analysis. In: Proceedings of the IEEE EMBC, pp. 1077–1080 (2013)
Wu, W., Chen, A.Y.C., Zhao, L., Corso, J.J.: Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features. Int. J. Comput. Assist. Radiol. Surg. 9(2), 241–253 (2013). https://doi.org/10.1007/s11548-013-0922-7
Zhuge, Y., Krauze, A.V., Ning, H., Cheng, J.Y., Arora, B.C., Camphausen, K., Miller, R.W.: Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 44, 5234–5243 (2017)
Zikic, D., et al.: Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 369–376. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33454-2_46
Acknowledgment
JN was supported by the Silesian University of Technology funds through the grant for maintaining and developing research potential.
This paper is in memory of Dr. Grzegorz Nalepa, an extraordinary scientist and pediatric hematologist/oncologist at Riley Hospital for Children, Indianapolis, USA, who helped countless patients and their families through some of the most challenging moments of their lives.
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Kotowski, K., Adamski, S., Machura, B., Zarudzki, L., Nalepa, J. (2022). Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_18
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