3D brain image segmentation using 3D tiled convolution neural networks

Gliomas are the primary brain tumors that are most commonly observed in adult patients and exhibit varying degrees of aggressiveness and prognosis. The accurate identification and diagnosis of Gliomas in surgical procedures heavily rely on the acquisition of precise segmentation results, which involve delineating the tumor region from magnetic resonance imaging (MRI) scans of the brain. The segmentation process in conventional 3D CNN methods is often reliant on patch processing as a result of the limitations in GPU memory. This paper presents an approach for segmenting brain tumors into distinct subregions, namely the whole tumor, tumor core, and enhancing tumor, utilizing a 3D tiled convolution-based segmentation method. The utilization of the 3DTC method enables the inclusion of larger patch sizes without requiring hardware with high GPU memory. This study presents three significant modifications to the standard 3D U-Net. Firstly, we incorporate 3D tiled convolution as the initial layer in our proposed models. Secondly, we substitute the trilinear upsampling layer with a dense upsampling convolution layer. Lastly, we replace the standard convolution block with recurrent residual blocks in the proposed R2AU-Net. The best framework was utilized to apply an average ensembling technique, aiming to achieve accurate results on the validation set of the BraTS 2020 dataset. The network proposed in this study was utilized for the analysis of the BRATS2020 dataset. The evaluation of our method on the validation dataset yielded Dice scores of 90.76%, 83.39%, and 74.77% for the whole tumor, tumor core, and enhancing tumor region, respectively.