SegCoFusion: An Integrative Multimodal Volumetric Segmentation Cooperating With Fusion Pipeline to Enhance Lesion Awareness

Multimodal volumetric segmentation and fusion are two valuable techniques for surgical treatment planning, image-guided interventions, tumor growth detection, radiotherapy map generation, etc. In recent years, deep learning has demonstrated its excellent capability in both of the above tasks, while these methods inevitably face bottlenecks. On the one hand, recent segmentation studies, especially the U-Net-style series, have reached the performance ceiling in segmentation tasks. On the other hand, it is almost impossible to capture the ground truth of the fusion in multimodal imaging, due to differences in physical principles among imaging modalities. Hence, most of the existing studies in the field of multimodal medical image fusion, which fuse only two modalities at a time with hand-crafted proportions, are subjective and task-specific. To address the above concerns, this work proposes an integration of multimodal segmentation and fusion, namely SegCoFusion, which consists of a novel feature frequency dividing network named FDNet and a segmentation part using a dual-single path feature supplementing strategy to optimize the segmentation inputs and suture with the fusion part. Furthermore, focusing on multimodal brain tumor volumetric fusion and segmentation, the qualitative and quantitative results demonstrate that SegCoFusion can break the ceiling both of segmentation and fusion methods. Moreover, the effectiveness of the proposed framework is also revealed by comparing it with state-of-the-art fusion methods on 2D two-modality fusion tasks, our method achieves better fusion performance than others. Therefore, the proposed SegCoFusion develops a novel perspective that improves the performance in volumetric fusion by cooperating with segmentation and enhances lesion awareness.