Accurate volumetric segmentation of brain tumors and tissues is beneficial for quantitative brain analysis and brain disease identification in multi-modal Magnetic Resonance (MR) images. Nevertheless, due to the complex relationship between modalities, 3D Fully Convolutional Networks (3D FCNs) using simple multi-modal fusion strategies hardly learn the complex and nonlinear complementary information between modalities. Meanwhile, the indiscriminative feature aggregation between low-level and high-level features easily causes volumetric feature misalignment in 3D FCNs. On the other hand, the 3D convolution operations of 3D FCNs are excellent at modeling local relations but typically inefficient at capturing global relations between distant regions in volumetric images. To tackle these issues, we propose an Aligned Cross-Modality Interaction Network (ACMINet) for segmenting the regions of brain tumors and tissues from MR images. In this network, the cross-modality feature interaction module is first designed to adaptively and efficiently fuse and refine multi-modal features. Secondly, the volumetric feature alignment module is developed for dynamically aligning low-level and high-level features by the learnable volumetric feature deformation field. Thirdly, we propose the volumetric dual interaction graph reasoning module for graph-based global context modeling in spatial and channel dimensions. Our proposed method is applied to brain glioma, vestibular schwannoma, and brain tissue segmentation tasks, and we performed extensive experiments on BraTS2018, BraTS2020, Vestibular Schwannoma, and iSeg-2017 datasets. Experimental results show that ACMINet achieves state-of-the-art segmentation performance on all four benchmark datasets and obtains the highest DSC score of hard-segmented enhanced tumor region on the validation leaderboard of the BraTS2020 challenge.

Brain tissue segmentation in multi-modal magnetic resonance (MR) images is significant for the clinical diagnosis of brain diseases. Due to blurred boundaries, low contrast, and intricate anatomical relationships between brain tissue regions, automatic brain tissue segmentation without prior knowledge is still challenging. This paper presents a novel 3D fully convolutional network (FCN) for brain tissue segmentation, called APRNet. In this network, we first propose a 3D anisotropic pyramidal convolutional reversible residual sequence (3DAPC-RRS) module to integrate the intra-slice information with the inter-slice information without significant memory consumption; secondly, we design a multi-modal cross-dimension attention (MCDA) module to automatically capture the effective information in each dimension of multi-modal images; then, we apply 3DAPC-RRS modules and MCDA modules to a 3D FCN with multiple encoded streams and one decoded stream for constituting the overall architecture of APRNet. We evaluated APRNet on two benchmark challenges, namely MRBrainS13 and iSeg-2017. The experimental results show that APRNet yields state-of-the-art segmentation results on both benchmark challenge datasets and achieves the best segmentation performance on the cerebrospinal fluid region. Compared with other methods, our proposed approach exploits the complementary information of different modalities to segment brain tissue regions in both adult and infant MR images, and it achieves the average Dice coefficient of 87.22% and 93.03% on the MRBrainS13 and iSeg-2017 testing data, respectively. The proposed method is beneficial for quantitative brain analysis in the clinical study, and our code is made publicly available.

Unsupervised domain adaptation (UDA) methods have achieved promising performance in alleviating the domain shift between different imaging modalities. In this paper, we propose a robust two-stage 3D Anatomy-guided Self-Training Cross-Modality Segmentation (ASTCMSeg) framework based on UDA for unpaired cross-modality image segmentation, including the anatomy-guided image translation and self-training segmentation stages. In the translation stage, we first leverage the similarity distributions between patches to capture the latent anatomical relationships and propose an anatomical relation consistency for preserving the correct anatomical relationships. Then, we design a frequency domain constraint to enforce the consistency of important frequency components during image translation. Finally, we integrate the anatomical relation consistency and frequency domain constraint with contrastive learning for anatomy-guided image translation. In the segmentation stage, we propose a context-aware anisotropic mesh network for segmenting anisotropic volumes in the target domain. Meanwhile, we design a volumetric adaptive self-training method that dynamically selects appropriate pseudo-label thresholds to learn the abundant label information from unlabeled target volumes. Our proposed method is validated on the cross-modality brain structure, cardiac substructure, and abdominal multi-organ segmentation tasks. Experimental results show that our proposed method achieves state-of-the-art performance in all tasks and significantly outperforms other 2D-based or 3D-based UDA methods.