Abstract
BACKGROUND: Osteosarcoma, the most common primary bone tumor originating from osteoblasts, poses a significant challenge in medical practice, particularly among adolescents. Conventional diagnostic methods heavily rely on manual analysis of magnetic resonance imaging (MRI) scans, which often fall short in providing accurate and timely diagnosis. This underscores the critical need for advancements in medical imaging technologies to improve the detection and characterization of osteosarcoma. METHODS: In this study, we sought to address the limitations of current diagnostic approaches by leveraging Hoechst-stained images of osteosarcoma cells obtained via fluorescence microscopy. Our primary objective was to enhance the segmentation of osteosarcoma cells, a crucial step in precise diagnosis and treatment planning. Recognizing the shortcomings of existing feature extraction networks in capturing detailed cellular structures, we propose a novel approach utilizing a twin swin transformer architecture for osteosarcoma cell segmentation, with a focus on multi-scale feature fusion. RESULTS: The experimental findings demonstrate the effectiveness of the proposed Twin Swin Transformer with multi-scale feature fusion in significantly improving osteosarcoma cell segmentation. Compared to conventional techniques, our method achieves superior segmentation performance, highlighting its potential utility in clinical settings. CONCLUSION: The development of our Twin Swin Transformer with multi-scale feature fusion method represents a significant advancement in medical imaging technology, particularly in the field of osteosarcoma diagnosis. By harnessing advanced computational techniques and leveraging high-resolution imaging data, our approach offers enhanced accuracy and efficiency in osteosarcoma cell segmentation, ultimately facilitating better patient care and clinical decision-making.