Abstract
Gliomas are the most common primary malignant brain tumors. Accurate segmentation and quantitative analysis of brain tumor are critical for diagnosis and treatment planning. Automatically segmenting tumors and their subregions is a challenging task as demonstrated by the annual Multimodal Brain Tumor Segmentation Challenge (BraTS). In order to tackle this challenging task, we trained 2D non-local Mask R-CNN with 814 patients from the BraTS 2021 training dataset. Our performance on another 417 patients from the BraTS 2021 training dataset were as follows: DSC of 0.784, 0.851 and 0.817; sensitivity of 0.775, 0.844 and 0.825 for the enhancing tumor, whole tumor and tumor core, respectively. By applying the focal loss function, our method achieved a DSC of 0.775, 0.885 and 0.829, as well as sensitivity of 0.757, 0.877 and 0.801. We also experimented with data distillation to ensemble single model’s predictions. Our refined results were DSC of 0.797, 0.884 and 0.833; sensitivity of 0.820, 0.855 and 0.820.
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
References
Bakas, S., et al.: Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features. Scientific Data 4(1), 1–13 (2017)
Pallud, J., et al.: Quantitative morphological magnetic resonance imaging follow-up of low-grade glioma: a plea for systematic measurement of growth rates. Neurosurgery 71(3), 729–740 (2012)
Bakas, S., et al.: Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge, arXiv preprint arXiv:1811.02629 (2018)
Huang, M., Yang, W., Wu, Y., Jiang, J., Chen, W., Feng, Q.: Brain tumor segmentation based on local independent projection-based classification. IEEE Trans. Biomed. Eng. 61(10), 2633–2645 (2014)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Gould, S., Gao, T., Koller, D.: Region-based segmentation and object detection. In: NIPS, vol. 1. (2009)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Wu, Y., Zhao, Z., Wu, W., Lin, Y., Wang, M.: Automatic glioma segmentation based on adaptive superpixel. BMC Med. Imaging 19(1), 1–14 (2019)
Baid, U., et al.: The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. arXiv preprint arXiv:2107.02314 (2021)
Menze, B., Reyes, M., Van Leemput, K.: The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans. Medical Imaging 34, 1–32 (2014)
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-GBM collection. Can. Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.KLXWJJ1Q
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., et al.: Segmentation labels and radiomic features for the pre-operative scans of the TCGA-LGG collection. Can. Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
Tustison, N.J., et al.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, pp. 7794–7803 (2018)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision 2017, pp. 2980–2988 (2017)
Radosavovic, I., Dollár, P., Girshick, R., Gkioxari, G., He, K.: Data distillation: towards omni-supervised learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018, pp. 4119–4128 (2018)
Abdulla, W.: Mask R-CNN for object detection and instance segmentation on keras and tensorflow (2017)
Amorim, P.H., et al.: 3D u-nets for brain tumor segmentation in MICCAI 2017 brats challenge. In: Proceedings of MICCAI Workshop on Multimodal Brain Tumor Segmentation Challenge (BRATS) (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Dai, Z., Wen, N., Carver, E. (2022). Brain Tumor Segmentation Using Non-local Mask R-CNN and Single Model Ensemble. 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_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-08999-2_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08998-5
Online ISBN: 978-3-031-08999-2
eBook Packages: Computer ScienceComputer Science (R0)
