Abstract
An automatic overall survival time prediction system for Glioma brain tumor patients is proposed and developed based on volumetric, location, and shape features. The proposed automatic prediction system consists of three stages: segmentation of brain tumor sub-regions; features extraction; and overall survival time predictions. A deep learning structure based on a modified 3 Dimension (3D) U-Net is proposed to develop an accurate segmentation model to identify and localize the three Glioma brain tumor sub-regions: gadolinium (GD)-enhancing tumor, peritumoral edema, and necrotic and non-enhancing tumor core (NCR/NET). The best performance of a segmentation model is achieved by the modified 3D U-Net based on an Accumulated Encoder (U-Net AE) with a Generalized Dice-Loss (GDL) function trained by the ADAM optimization algorithm. This model achieves Average Dice-Similarity (ADS) scores of 0.8898, 0.8819, and 0.8524 for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET), respectively, in the train dataset of the Multimodal Brain Tumor Segmentation challenge (BraTS) 2020. Various combinations of volumetric (based on brain functionality regions), shape, and location features are extracted to train an overall survival time classification model using a Neural Network (NN). The model classifies the data into three classes: short-survivors, mid-survivors, and long-survivors. An information fusion strategy based on features-level fusion and decision-level fusion is used to produce the best prediction model. The best performance is achieved by the ensemble model and shape features model with accuracies of (55.2%) on the BraTS 2020 validation dataset. The ensemble model achieves a competitive accuracy (55.1%) on the BraTS 2020 test dataset.
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References
Suchorska, B., et al.: Complete resection of contrast-enhancing tumor volume is associated with improved survival in recurrent glioblastoma - results from the DIRECTOR trial. Neuro-Oncology 18 (4), 549–556 (01 2016). https://doi.org/10.1093/neuonc/nov326
Brain Tumor: Grades and Prognostic Factors, Approved by the Cancer.Net Editorial Board. https://www.cancer.net/cancer-types/brain-tumor/grades-and-prognostic-factors. Accessed 13 Aug 2020
Menze, B., 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
Litjens, G., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
Esteva, A., et al.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017)
Esteva, A., et al.: A guide to deep learning in healthcare. Nat. Med. 25, 24–29 (2019)
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
Milletari, F.; Navab, N.; Ahmadi, S.A.:V-Net: fully convolutional neural networks for volumetric medical image segmentation, In: 3D Vision Fourth International Conference (3DV), Stanford, California, USA, October 25–28 (2016)
Chen, H., Dou, Q., Yu, L., Qin, J., Heng, P.A.: VoxResNet: deep voxelwise residual networks for brain segmentation from 3D MR Images, J. NeuroImage, 170, 446–455 (2018)
Liu, S., et al.: Prostate cancer diagnosis using deep learning with 3D multiparametric MRI, In: Medical Imaging Computer-Aided Diagnosis Conference, vol. 10134, Orlando, Florida, United States March 2017
Myronenko, A.: 3D MRI brain tumor segmentation using autoencoder regularization. In: Crimi, A., Bakas, S., Kuijf, H., K, Farahani, Reyes, M., van Walsum, Theo (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 311–320. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_28
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS -2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67558-9_28
Feng, X., Tustison, N., Meyer, C.: Brain tumor segmentation using an ensemble of 3d u-nets and overall survival prediction using radiomic features. BrainLes 2018, pp. 279–28876, Springer LNCS11384 (2019)
Weninger, L., Rippel, O., Koppers, S., Merhof, D.: Segmentation of brain tumors and patient survival prediction: methods for the brats 2018 challenge. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 3–12. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_1
Sun, L., Zhang, S., Luo, L.: Tumor segmentation and survival prediction in glioma with deep learning. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 83–93. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_8
Baid, U., et al.: Deep Learning Radiomics Algorithm for Gliomas (DRAG) Model: A Novel Approach Using 3D UNET Based Deep Convolutional Neural Network for Predicting Survival in Gliomas. In: Crimi, A., Bakas, S., Kuijf, H., Keyvan, F., Reyes, M., van Walsum, T. (eds.) BrainLes 2018. LNCS, vol. 11384, pp. 369–379. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11726-9_33
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J.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., Reyes, M., Jakab, A., Bauer, S., Rempfler, M., Crimi, A., 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)
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. Cancer Imaging Arch. (2017). https://doi.org/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. Cancer Imaging Arch. (2017). https://doi.org/10.7937/K9/TCIA.2017.GJQ7R0EF
“Parts of the Brain & Function”, Website: Anatomy info. https://anatomyinfo.com/parts-of-the-brain/. Accessed 20 July 2020
Acknowledgments
This research was supported by HOWE Foundation Fellowship/College of Engineering-UNLV and Summer Doctoral Research Fellowship/Graduate College-UNLV. We would like to thank Dr. Edwin Oh, Associate Professor, Neurogenetics and Precision Medicine Lab/UNLV, for his valuable help by providing a computer with Titan RTX GPU to train and test the Deep Learning segmentation models.
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Chato, L., Kachroo, P., Latifi, S. (2021). An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients Based on Volumetric and Shape Features. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12659. Springer, Cham. https://doi.org/10.1007/978-3-030-72087-2_31
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