Fig 1.
Two examples of multimodal image slices with ground truth from BraTS2018.
In this figure, green represents GDEnhancing Tumor (numerical label 2), yellow represents Pertumoral Edema (numerical label 1), and red represents Necrotic and Non-Enhancing Tumor Core(NCR/ECT, numerical label 4) showing the differences in texture, size and shape of primary brain tumours.
Fig 2.
Diagram of the proposed method model, which has an encoder-decoder architecture.
Fig 3.
Hierarchical Decoupled Convolution(HDC) module.
Fig 4.
Feature interaction module.
Fig 5.
Global attention mechanism.
Table 1.
Quantitative results of the proposed method on the Brats2019 validation set, including Dice and HD95.
Table 2.
Quantitative results of the proposed method on the Brats2018 validation set, including Dice and HD95.
Fig 6.
Segmentation results obtained by applying our proposed method to four cases on the BraTS2019 dataset.
From left to right: (a,b) Flair and T2 slices, (c,f) 2D ground truth overlaid on T2 slices, ET: Yellow; TC: Yellow + Red; WT: Yellow + Red + Green.
Table 3.
Ablation study of the proposed method on the Brats2019 validation dataset with/without the feature interaction module and the attention module.
Performance is measured in Dice (%) and Hausdorff distance (mm).
Fig 7.
Visualisation results superimposed on T2 slices at different latitudes, the horizontal axis represents the different latitudes, the last column shows the 3D visualisation results, the vertical axis represents the results of the different methods and the last row shows the ground truth mask.
Fig 8.
Samples of Multimodal Brain Tumor Segmentation on BraTS 2019, from left to right, are the segmentation feature maps generated after the addition of the attention mechanism, and we have selected the feature slices from different periods for easier observation, where green represents WT, blue represents ET, and red represents TC.
Table 4.
Compare our method with the methods commonly used on the BraTS 2018 dataset.