Computer Science Faculty Publications and Presentations
Document Type
Article
Publication Date
2-25-2025
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.
Recommended Citation
Dai, Siyuan, Kai Ye, Charlie Zhan, Haoteng Tang, and Liang Zhan. "SIN-Seg: A joint spatial-spectral information fusion model for medical image segmentation." Computational and Structural Biotechnology Journal 27 (2025): 744-752. https://doi.org/10.1016/j.csbj.2025.02.024
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Title
Computational and Structural Biotechnology Journal
DOI
10.1016/j.csbj.2025.02.024
Comments
© 2025 University of Pittsburgh. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. http://creativecommons.org/licenses/by-nc-nd/4.0/