Gradient-Guided Network With Fourier Enhancement for Glioma Segmentation in Multimodal 3D MRI.

Abstract

Glioma segmentation is a crucial task in computer-aided diagnosis, requiring precise discrimination between lesions and normal tissue at the pixel level. Popular methods neglect crucial edge information, leading to inaccurate contour delineation. Moreover, global information has been proven beneficial for segmentation. The feature representations extracted by convolution neural networks often struggle with local-related information owing to the limited receptive fields. To address these issues, we propose a novel edge-aware segmentation network that incorporates a dual-path gradient-guided training strategy with Fourier edge-enhancement for precise glioma segmentation, a.k.a. GFNet. First, we introduce a Dual-path Gradient-guided Training strategy (DGT) based on a Siamese network guiding the optimizing direction of one path by the gradient from the other path. DGT pays attention to the indistinguishable pixels with large weight-updating gradient, such as the pixels near the boundary, to guide the network training, addressing hard samples. Second, to further perceive the edge information, we derive a Fourier Edge-enhancement Module (FEM) to augment feature edges with high-frequency representations from the spectral domain, providing global information and edge details. Extensive experiments on public glioma segmentation datasets, BraTS2020 and Medical Segmentation Decathlon (MSD) glioma and prostate segmentation, demonstrate that GFNet achieves competitive performance compared to other state-of-the-art methods, both qualitatively and quantitatively.