Deep learning evaluation demonstrates high-precision semantic segmentation in artifact-affected CT scans, highlighting improved diagnostic accuracy near metallic implants.
The semantic segmentation for computed tomography (CT) images is an important step in clinical diagnosis. Metal artifacts may cause significant segmentation errors, and even after artifact reduction, conventional methods still struggle to achieve accurate segmentation results. To address the reduction in segmentation accuracy caused by artifact interference, this paper proposed a Multi-Scale Detail Enhanced Network (MSD-Net). The network employed a Channel Multi-Scale module and a Mamba module to extract multi-scale information and selectively capture input information, establishing clear relationships between global and local features. When processing blurred edge information, the proposed multi-scale detail enhancement structure enhanced the extracted multi-scale detail information, and used a multi-scale feature fusion method to extract semantic information at more appropriate scales. High-precision segmentation results were achieved on images following metal artifact reduction.
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Zhang et al. (2024) studied this question.
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