Abstract
Automatic segmentation of brain glioma from multimodal MRI scans plays a key role in clinical trials and practice. Unfortunately, manual segmentation is very challenging, time-consuming, costly, and often inaccurate despite human expertise due to the high variance and high uncertainty in the human annotations. In the present work, we develop an end-to-end deep-learning-based segmentation method using a multi-decoder architecture by jointly learning three separate sub-problems using a partly shared encoder. We also propose to apply smoothing methods to the input images to generate denoised versions as additional inputs to the network. The validation performance indicates an improvement when using the proposed method. The proposed method was ranked 2nd in the task of Quantification of Uncertainty in Segmentation in the Brain Tumors in Multimodal Magnetic Resonance Imaging Challenge 2020.
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Acknowledgement
The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at the HPC2N in Umeå, Sweden. We are grateful for the financial support obtained from the Cancer Research Fund in Northern Sweden, Karin and Krister Olsson, Umeå University, The Västerbotten regional county, and Vinnova, the Swedish innovation agency.
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Vu, M.H., Nyholm, T., Löfstedt, T. (2021). Multi-decoder Networks with Multi-denoising Inputs for Tumor Segmentation. 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_37
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