Abstract
Another year of the multimodal brain tumor segmentation challenge (BraTS) 2021 provides an even larger dataset to facilitate collaboration and research of brain tumor segmentation methods, which are necessary for disease analysis and treatment planning. A large dataset size of BraTS 2021 and the advent of modern GPUs provide a better opportunity for deep-learning based approaches to learn tumor representation from the data. In this work, we maintained an encoder-decoder based segmentation network, but focused on a modification of network training process that minimizes redundancy under perturbations. Given a set trained networks, we further introduce a confidence based ensembling techniques to further improve the performance. We evaluated the method on BraTS 2021, and in terms of dice for enhanced tumor core, tumor core and whole tumor, we achieved 0.8600, 0.8868 and 0.9265 average dice for the validation set, and 0.8769, 0.8721, 0.9266 average dice for the testing set. Our team (NVAUTO) submission was the top performing in terms of ET and TC scores, and using the Brats ranking system (based on the dice and Hausdorff distance ranking per case) achieved the 2nd place on the validation set, and the 4th place on the testing set.
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Rahman Siddiquee, M.M., Myronenko, A. (2022). Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2021. Lecture Notes in Computer Science, vol 12963. Springer, Cham. https://doi.org/10.1007/978-3-031-09002-8_15
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