  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 indicate 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.
