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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.
Computational Intelligence and Neuroscience
|August 15, 2022
Summary
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.
