Abstract
BACKGROUND: The limitation in spatial resolution of bone scintigraphy, combined with the vast variations in size, location, and intensity of bone metastasis (BM) lesions, poses challenges for accurate diagnosis by human experts. Deep learning-based analysis has emerged as a preferred approach for automating the identification and delineation of BM lesions. This study aims to develop a deep learning-based approach to automatically segment bone scintigrams for improving diagnostic accuracy. METHODS: This study introduces a deep learning-based segmentation model structured around an encoder-decoder architecture. The model employs a multi-attention learning scheme to enhance the contrast of the skeleton outline against the background and a multi-scale learning strategy to highlight the hotspots within skeletal areas. The multi-attention strategies include the Non-local Attention scheme and the vision transformer (ViT), while the multi-scale learning incorporates the multi-scale feature learning strategy and the multi-pooling learning strategy. This combination enables the proposed model to accurately detect and extract lesions of varying sizes with high randomness in location and intensity. RESULTS: Experimental evaluation conducted on clinical data of single photon emission computed tomography (SPECT) bone scintigrams showed the superior performance of the proposed model, achieving the highest-ever dice similarity coefficient (DSC) score of 0.6720. A comparative analysis on the same dataset demonstrated increased scores of 5.6%, 2.03%, and 7.9% for DSC, Precision, and Recall, respectively, compared to the existing models. CONCLUSIONS: The proposed segmentation model can be used as a promising tool for automatically extracting metastasis lesions from SPECT bone scintigrams, offering significant support to the development of deep learning-based automated analysis for characterizing BM.