  We developed a deep ensemble learning model with a radiomics spatial encoding
execution for improved glioma segmentation accuracy using multi-parametric MRI
(mp-MRI). This model was developed using 369 glioma patients with a 4-modality
mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each
modality volume, a 3D sliding kernel was implemented across the brain to
capture image heterogeneity: fifty-six radiomic features were extracted within
the kernel, resulting in a 4th order tensor. Each radiomic feature can then be
encoded as a 3D image volume, namely a radiomic feature map (RFM). PCA was
employed for data dimension reduction and the first 4 PCs were selected. Four
deep neural networks as sub-models following the U-Net architecture were
trained for the segmenting of a region-of-interest (ROI): each sub-model
utilizes the mp-MRI and 1 of the 4 PCs as a 5-channel input for a 2D execution.
The 4 softmax probability results given by the U-net ensemble were superimposed
and binarized by Otsu method as the segmentation result. Three ensemble models
were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor
(WT). The adopted radiomics spatial encoding execution enriches the image
heterogeneity information that leads to the successful demonstration of the
proposed deep ensemble model, which offers a new tool for mp-MRI based medical
image segmentation.
