Abstract
Segmentation of Glioma from three dimensional magnetic resonance imaging (MRI) is useful for diagnosis and surgical treatment of patients with brain tumor. Manual segmentation is expensive, requiring medical specialists. In the recent years, the Brain Tumor Segmentation Challenge (BraTS) has been calling researchers to submit automated glioma segmentation and survival prediction methods for evaluation and discussion over their public, multimodality MRI dataset, with manual annotations. This work presents an exploration of different solutions to the problem, using 3D UNets and self attention for multitasking both predictions and also training (2D) EfficientDet derived segmentations, with the best results submitted for the official challenge leaderboard. We show that end-to-end multitasking survival and segmentation, in this case, led to better results.
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Acknowledgement
We thank Israel Campiotti from NeuralMind for his help in the implementation of the modified EfficientDet architecture. D Carmo thanks the support from São Paulo Research Foundation (FAPESP) grant 2019/21964-4. R Lotufo thanks CNPq for the grant 310828/2018-0. This work is also supported by grant 2013/07559-3 (FAPESP-BRAINN).
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Carmo, D., Rittner, L., Lotufo, R. (2021). MultiATTUNet: Brain Tumor Segmentation and Survival Multitasking. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_38
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DOI: https://doi.org/10.1007/978-3-030-72084-1_38
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