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A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries
Jia Wu1,2, Luting Zhou1, Fangfang Gou1
1School of Computer Science and Engineering, Central South University, Chang Sha 410083, China.
Abstract:
Among primary bone cancers, osteosarcoma is the most common, peaking between the ages of a child's rapid bone growth and adolescence. The diagnosis of osteosarcoma requires observing the radiological appearance of the infected bones. A common approach is MRI, but the manual diagnosis of MRI images is prone to observer bias and inaccuracy and is rather time consuming. The MRI images of osteosarcoma contain semantic messages in several different resolutions, which are often ignored by current segmentation techniques, leading to low generalizability and accuracy. In the meantime, the boundaries between osteosarcoma and bones or other tissues are sometimes too ambiguous to separate, making it a challenging job for inexperienced doctors to draw a line between them. In this paper, we propose using a multiscale residual fusion network to handle the MRI images. We placed a novel subnetwork after the encoders to exchange information between the feature maps of different resolutions, to fuse the information they contain. The outputs are then directed to both the decoders and a shape flow block, used for improving the spatial accuracy of the segmentation map. We tested over 80,000 osteosarcoma MRI images from the PET-CT center of a well-known hospital in China. Our approach can significantly improve the effectiveness of the semantic segmentation of osteosarcoma images. Our method has higher F1, DSC, and IOU compared with other models while maintaining the number of parameters and FLOPS.
Insights
This study introduces a novel multiscale residual fusion network for improved osteosarcoma MRI segmentation. The new method enhances diagnostic accuracy by effectively integrating multi-resolution image data, outperforming existing techniques.
Area of Science:
- Oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Osteosarcoma is the most common primary bone cancer, often diagnosed via MRI.
- Manual MRI diagnosis is time-consuming, subjective, and can be inaccurate.
- Current segmentation methods struggle with multi-resolution data and ambiguous boundaries in osteosarcoma MRI.
Purpose of the Study:
- To develop an advanced deep learning model for accurate osteosarcoma semantic segmentation in MRI.
- To address limitations of existing methods in handling multi-resolution features and unclear tissue margins.
Main Methods:
- Proposed a multiscale residual fusion network incorporating a novel subnetwork for inter-resolution feature exchange.
- Integrated a shape flow block to enhance spatial accuracy of segmentation maps.
- Validated the model on over 80,000 osteosarcoma MRI images.
Main Results:
- The proposed network significantly improved semantic segmentation effectiveness for osteosarcoma MRI.
- Achieved higher F1, DSC, and IOU scores compared to existing models.
- Maintained comparable model parameters and FLOPS.
Conclusions:
- The multiscale residual fusion network offers a more effective and accurate approach for osteosarcoma MRI segmentation.
- This method has the potential to aid in more reliable and efficient diagnosis of osteosarcoma.
