Abstract
Accurate and reproducible segmentation of brain tumors from multi-modal magnetic resonance (MR) scans is a pivotal step in practice. In this BraTS Continuous Evaluation initiative, we exploit a 3D nnU-Net for this task which was ranked at the \(6^\textrm{th}\) place (out of 1600 participants) in the BraTS’21 Challenge. We benefit from an ensemble of deep models enhanced with the expert knowledge of a senior radiologist captured in a form of several post-processing routines. The experimental study showed that infusing the domain knowledge into the algorithm can enhance their performance, and we obtained the average Dice score of 0.81977 (enhancing tumor), 0.87837 (tumor core), and 0.92723 (whole tumor) over the validation set. For the test data, we had the average Dice score of 0.86317, 0.87987, and 0.92838 for the enhancing tumor, tumor core and whole tumor. To validate the generalization capabilities of the nnU-Nets enhanced with domain knowledge, we performed their federated evaluation within the Federated Tumor Segmentation (FeTS) 2022 Challenge over the datasets captured across 30 institutions. Our technique was ranked \(2^\textrm{nd}\) across all participating teams, proving its generalization capabilities over unseen out-of-sample datasets.
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Notes
- 1.
Our team name is Graylight Imaging.
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 (2017). The Cancer Imaging Archive. 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 (2017). The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Bakas, S., et al.: Identifying the bestmachine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge. CoRR abs/1811.02629 (2018). arXiv.org: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)
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
Kotowski, K., Adamski, S., Machura, B., Zarudzki, L., Nalepa, J.: 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, pp. 197–209. Springer International Publishing, Cham (2022). https://doi.org/10.1007/978-3-031-09002-8_18
Menze, B.H., et al.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Med. Imaging 34(10), 1993–2024 (2015)
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., et al.: Data augmentation via image registration. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 4250–4254, September 2019. https://doi.org/10.1109/ICIP.2019.8803423
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)
Pati, S., et al.: The Federated Tumor Segmentation (FeTS) Challenge (2021). https://doi.org/10.48550/ARXIV.2105.05874, arXiv.org:2105.05874
Pipitone, J., et al.: Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101, 494–512 (2014)
Reina, G.A., et al.: OpenFL: an open-source framework for Federated Learning (2021). https://doi.org/10.48550/ARXIV.2105.06413, arXiv.org:2105.06413
Sheller, M.J., et al.: Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10(1), 12598 (2020)
Simi, V., Joseph, J.: Segmentation of glioblastoma multiforme from MR images - a comprehensive review. Egypt. J. Radiol. Nucl. Med. 46(4), 1105–1110 (2015)
Wijata, A.M., Nalepa, J.: Unbiased validation of the algorithms for automatic needle localization in ultrasound-guided breast biopsies. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3571–3575 (2022). https://doi.org/10.1109/ICIP46576.2022.9897449
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 (2014)
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
Acknowledgments
JN was supported by the Silesian University of Technology funds through the grant for maintaining and developing research potential. This work was supported by the Polish National Centre for Research and Development grant: POIR.01.01.01-00-0092/20 (Methods and algorithms for automatic coronary artery calcium scoring on cardiac computed tomography scans).
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., Malara, W., Zarudzki, L., Nalepa, J. (2023). Federated Evaluation of nnU-Nets Enhanced with Domain Knowledge for Brain Tumor Segmentation. In: Bakas, S., et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 14092. Springer, Cham. https://doi.org/10.1007/978-3-031-44153-0_21
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