Multi-Decoder Networks with Multi-Denoising Inputs for Tumor Segmentation
This incremental improvement addresses time-consuming and inaccurate manual tumor segmentation for clinical applications.
The authors tackled brain glioma segmentation from multimodal MRI scans by developing a multi-decoder network with denoised inputs, achieving 2nd place in the BraTS 2020 challenge's uncertainty quantification task.
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.