A paired multi-scale attention network for liver tumor segmentation in 99mTc-MAA SPECT/CT imaging

Abstract Automatic liver tumor segmentation using Single-Photon Emission Computed Tomography/Computed Tomography (SPECT/CT) provides detailed insights that facilitate accurate tumor targeting and effective treatment planning. However, challenges such as spill-out can distort tumor size, which complicates accurate segmentation. This study introduces a Paired Multi-Scale Attention Network (P-MANet) using Top-Hat Cross-Features to address these challenges by enhancing the identification of true positives while minimizing false positives in liver tumor segmentation. P-MANet employs a dual-branch architecture. The first branch utilizes a Multi-Scale Attention Network (MA-Net) to process SPECT/CT datasets, whereas the second branch applies the White Top-Hat Transform to extract features that are then integrated with those from the first branch. This innovative approach effectively mitigates errors stemming from spectral light variation, leading to improved accuracy in tumor delineation. The model was trained on a dataset comprising 43 cases of 99mTc-MAA SPECT/CT. P-MANet achieved a Dice Similarity Coefficient (DSC) of 67.93% and 66.56% for normal and abnormal spectral light distributions, respectively, and obtained a DSC of 67.00%, on average, which outperformed other models, including results from a previous study that was tested on the same dataset.