Abstract
Brain tumor segmentation is a vital clinical requirement. In recent years, the developments of the prevalence of deep learning in medical image processing have been experienced. Automated brain tumor segmentation can reduce the diagnosis time and increase the potential of clinical intervention. In this work, we have used a patch selection methodology based on modified U-Net deep learning architecture with appropriate normalization and patch selection methods for the brain tumor segmentation task in BraTS 2020 challenge. Two-phase network training was implemented with patch selection methods. The performance of our deep learning-based brain tumor segmentation approach was done on CBICA’s Image Processing Portal. We achieved a Dice score of 0.795, 0.886, 0.827 in the testing phase, for the enhancing tumor, whole tumor, and tumor core respectively. The segmentation outcome with various radiomic features was used for Overall survival (OS) prediction. For OS prediction we achieved an accuracy of 0.570 for the testing phase. The algorithm can further be improved for tumor inter-class segmentation and OS prediction with various network implementation strategies. As the OS prediction results are based on segmentation, there is a scope of improvement in the segmentation and OS prediction thereby.
Computational support provided by Keepsake Welding Research and Skill Development Center, Center of Excellence - Welding, L. D. College of Engineering, Ahmedabad.
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Parmar, B., Parikh, M. (2021). Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified 3D U-Net. 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_35
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