Purpose Recently, brain tumor segmentation has made important progress. However, the quality of manual labels plays an important role in the performance, while in practice, it could vary greatly and in turn could substantially mislead the learning process and decrease the accuracy. We need to design a mechanism to combine label correction and sample reweighting to improve the effectiveness of brain tumor segmentation. Methods We propose a novel sample reweighting and label refinement method, and a novel three‐dimensional (3D) generative adversarial network (GAN) is introduced to combine these two models into an united framework. Results Extensive experiments on the BraTS19 dataset have demonstrated that our approach obtains competitive results when compared with other state‐of‐the‐art approaches when handling the false labels in brain tumor segmentation. Conclusions The 3D GAN‐based approach is an effective approach to handle false label masks by simultaneously applying label correction and sample reweighting. Our method is robust to variations in tumor shape and background clutter.
Purpose: Recently, research on brain tumor segmentation has made great progress. However, ambiguous patterns in magnetic resonance imaging data and linear fusion omitting semantic gaps between features in different branches remain challenging. We need to design a mechanism to fully utilize the similarity within the spatial space and channel space and the correlation between these two spaces to improve the result of volumetric segmentation. Methods: We propose a revised cascade structure network. In each subnetwork, a context exploitation module is introduced between the encoder and decoder, in which the dual attention mechanism is adopted to learn the information within the spatial space and channel space, and space interaction learning is employed to model the relation between the spatial and channel spaces. Results: Extensive experiments on the BraTS19 dataset have evaluated that our approach improves the dice coefficient (DC) by a margin of 2.1, 2.0, and 1.4 for whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively, obtaining results competitive with the state-of-art approaches working on brain tumor segmentation. Conclusions: Context exploitation in the embedding feature spaces, including intraspace relations and interspace relations, can effectively model dependency in semantic features and alleviate the semantic gap in multimodel data. Our approach is also robust to variations in different modality.
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