A Multiscale Patch Based Convolutional Network for Brain Tumor Segmentation
This work addresses the problem of accurate brain tumor segmentation for medical imaging applications, representing an incremental improvement with specific performance gains.
The authors tackled brain tumor segmentation in 3D MR images using a multiscale patch-based convolutional neural network, achieving dice scores of 0.755, 0.900, and 0.782 for enhanced tumor core, whole tumor, and tumor core, respectively, on the BRATS 2017 challenge.
This article presents a multiscale patch based convolutional neural network for the automatic segmentation of brain tumors in multi-modality 3D MR images. We use multiscale deep supervision and inputs to train a convolutional network. We evaluate the effectiveness of the proposed approach on the BRATS 2017 segmentation challenge where we obtained dice scores of 0.755, 0.900, 0.782 and 95% Hausdorff distance of 3.63mm, 4.10mm, and 6.81mm for enhanced tumor core, whole tumor and tumor core respectively.