Abstract
In recent years, the application of deep convolutional neural networks (DCNNs) to medical image segmentation has shown significant promise in computer-aided detection and diagnosis (CAD). Leveraging features from different spaces (i.e. Euclidean, non-Euclidean, and spectrum spaces) and multi-modalities of data have the potential to improve the information available to the CAD system, enhancing both effectiveness and efficiency. However, directly acquiring data from different spaces across multi-modalities is often prohibitively expensive and time-consuming. Consequently, most current medical image segmentation techniques are confined to the spatial domain, which is limited to utilizing scanned images from MRI, CT, PET, etc. Here, we introduce an innovative Joint Spatial-Spectral Information Fusion method which requires no additional data collection for CAD. We translate existing single-modality data into a new domain to extract features from an alternative space. Specifically, we apply Discrete Cosine Transformation (DCT) to enter the spectrum domain, thereby accessing supplementary feature information from an alternate space. Recognizing that information from different spaces typically necessitates complex alignment modules, we introduce a contrastive loss function for achieving feature alignment before synchronizing information across different feature spaces. Our empirical results illustrate the greater effectiveness of our model in harnessing additional information from the spectrum-based space and affirm its superior performance against influential state-of-the-art segmentation baselines. The code is available at https://github.com/Auroradsy/SIN-Seg.