A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation

Abstract

Purpose: 
To develop a deep ensemble learning (DEL) model with a radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric MRI (mp-MRI). 
Methods: 
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). For each patient, all RFMs extracted from all 4 modalities were processed by the Principal Component Analysis (PCA) for dimension reduction, and the first 4 principal components (PCs) were selected. Next, a DEL model comprised of four U-Net sub-models was 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 2D execution. Last, 4 softmax probability results given by the DEL model were superimposed and binarized by Otsu’s method as the segmentation result. Three DEL models were trained to segment enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. Segmentation results given by the proposed ensemble were compared to the mp-MRI-only U-Net results.
Results: 
All 3 radiomics-incorporated deep ensemble learning models were successfully implemented: comparing to mp-MRI-only U-net results, the dice coefficients of ET (0.777→0.817), TC (0.742→0.757), and WT (0.823→0.854) demonstrated improvements. Accuracy, sensitivity, and specificity results demonstrated the same patterns.
Conclusion: 
The adopted radiomics spatial encoding execution enriches the image heterogeneity information that leads to the successful demonstration of the proposed DEL model, which offers a new tool for mp-MRI based medical image segmentation.
Keywords: Radiomics, deep learning, deep ensemble learning, glioma segmentation