2D–3D cascade network for glioma segmentation in multisequence MRI images using multiscale information

• Published in: Computer methods and programs in biomedicine 2022-06, Vol.221, p.106894-106894, Article 106894
• Main Authors: Cao, Jianyun, Lai, Haoran, Zhang, Jiawei, Zhang, Junde, Xie, Tao, Wang, Heqing, Bu, Junguo, Feng, Qianjin, Huang, Meiyan
• Format: Article
• Language: English
• Subjects: 2D–3D cascade network; Glioma segmentation; Multiscale information; Multisequence MRI; Multitask learning
• ISSN: 0169-2607; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106894

•A 2D–3D cascade network with multi-scale information is proposed for glioma segmentation.•A multi-task learning-based 2D network is applied to exploit intra-slice features.•A 3D DenseUNet is integrated with the 2D network to extract inter-slice features.•A multi-scale information module is used in 2D and 3D networks to capture glioma details.•Competitive performance is achieved on public available and clinical datasets. Glioma segmentation is an important procedure for the treatment plan and follow-up evaluation of patients with glioma. UNet-based networks are widely used in medical image segmentation tasks and have achieved state-of-the-art performance. However, context information along the third dimension is ignored in 2D convolutions, whereas difference between z-axis and in-plane resolutions is large in 3D convolutions. Moreover, an original UNet structure cannot capture fine details because of the reduced resolution of feature maps near bottleneck layers. To address these issues, a novel 2D–3D cascade network with multiscale information module is proposed for the multiclass segmentation of gliomas in multisequence MRI images. First, a 2D network is applied to fully exploit potential intra-slice features. A variational autoencoder module is incorporated into 2D DenseUNet to regularize a shared encoder, extract useful information, and represent glioma heterogeneity. Second, we integrated 3D DenseUNet with the 2D network in cascade mode to extract useful inter-slice features and alleviate the influence of large difference between z-axis and in-plane resolutions. Moreover, a multiscale information module is used in the 2D and 3D networks to further capture the fine details of gliomas. Finally, the whole 2D–3D cascade network is trained in an end-to-end manner, where the intra-slice and inter-slice features are fused and optimized jointly to take full advantage of 3D image information. Our method is evaluated on publicly available and clinical datasets and achieves competitive performance in these two datasets. These results indicate that the proposed method may be a useful tool for glioma segmentation.