Abstract
This article aims to build a framework for MRI images of brain tumor segmentation using the deep learning method. For this purpose, we develop a novel 2-Phase UNet-based OMT framework to increase the ratio of brain tumors using optimal mass transportation (OMT). Moreover, due to the scarcity of training data, we change the density function by different parameters to increase the data diversity. For the post-processing, we propose an adaptive ensemble procedure by solving the eigenvectors of the Dice similarity matrix and choosing the result with the highest aggregation probability as the predicted label. The Dice scores of the whole tumor (WT), tumor core (TC), and enhanced tumor (ET) regions for online validation computed by SegResUNet were 0.9214, 0.8823, and 0.8411, respectively. Compared with random crop pre-processing, OMT is far superior.
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Acknowledgments
This work was partially supported by the Ministry of Science and Technology (MoST), the National Center for Theoretical Sciences, and Big Data Computing Center of Southeast University. W.-W. Lin and T.M. Huang were partially supported by MoST 110-2115-M-A49-004- and MoST 110-2115-M-003-012-MY3, respectively. T. Li was supported in part by the National Natural Science Foundation of China (NSFC) 11971105.
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Liao, JW., Huang, TM., Li, T., Lin, WW., Wang, H., Yau, ST. (2023). An UNet-Based Brain Tumor Segmentation Framework via Optimal Mass Transportation Pre-processing. In: Bakas, S., et al. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2022. Lecture Notes in Computer Science, vol 13769. Springer, Cham. https://doi.org/10.1007/978-3-031-33842-7_19
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