Abstract
This paper proposes a glioma segmentation method based on neural networks. The base of the network is a UNet, expanded by residual blocks. Several preprocessing steps were applied before training, such as intensity normalization, high intensity cutting, cropping, and random flips. 2D and 3D solutions are implemented and tested, and results show that the 3D network outperforms 2D directions, therefore we stayed with 3D directions.
The novelty of the method is the energy-based post-processing. Snakes [10], and conditional random fields (CRF) [11] were applied to the neural network’s predictions. Snake or active contour needs an initial outline around the object – e.g. the network’s prediction outline - and it can correct the contours of the tumor based on calculating the energy minimum, based on the intensity values at a given area. CRF is a specific type of graphical model, it uses the network’s prediction and the raw image features to estimate the posterior distribution (the tumor contour) using energy function minimization.
The proposed methods are evaluated within the framework of the BRATS 2020 challenge. Measured on the test dataset the mean dice scores of the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) are 86.9%, 83.2% and 81.8% respectively. The results show high performance and promising future work in tumor segmentation, even outside of the brain.
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Acknowledgement
This research is part of the Deep MR-only Radiation Therapy activity (project numbers: 19037, 20648) that has received funding from EIT Health. EIT Health is supported by the European Institute of Innovation and Technology (EIT), a body of the European Union receives support from the European Union´s Horizon 2020 Research and innovation programme.
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Zsamboki, R., Takacs, P., Deak-Karancsi, B. (2021). Glioma Segmentation with 3D U-Net Backed with Energy-Based Post-Processing. 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_10
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