Unified Attentional Generative Adversarial Network for Brain Tumor Segmentation From Multimodal Unpaired Images
This addresses the problem of segmenting brain tumors from unpaired images for clinical applications, offering an incremental improvement over existing methods.
The paper tackled brain tumor segmentation from unpaired multimodal images by proposing a unified attentional generative adversarial network (UAGAN) that performs modality translation and segmentation simultaneously, achieving superior performance compared to existing methods in most cases.
In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (UAGAN), which can perform any-to-any image modality translation and segment the target objects simultaneously in the case where two or more modalities are available. The translation stream is used to capture modality-invariant features of the target anatomical structures. In addition, to focus on segmentation-related features, we add attentional blocks to extract valuable features from the translation stream. Experiments on three-modality brain tumor segmentation indicate that UAGAN outperforms the existing methods in most cases.