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      Osteosarcoma MRI Image-Assisted Segmentation System Base on Guided Aggregated Bilateral Network

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      Mathematics
      MDPI AG

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          Abstract

          Osteosarcoma is a primary malignant tumor. It is difficult to cure and expensive to treat. Generally, diagnosis is made by analyzing MRI images of patients. In the process of clinical diagnosis, the mainstream method is the still time-consuming and laborious manual screening. Modern computer image segmentation technology can realize the automatic processing of the original image of osteosarcoma and assist doctors in diagnosis. However, to achieve a better effect of segmentation, the complexity of the model is relatively high, and the hardware conditions in developing countries are limited, so it is difficult to use it directly. Based on this situation, we propose an osteosarcoma aided segmentation method based on a guided aggregated bilateral network (OSGABN), which improves the segmentation accuracy of the model and greatly reduces the parameter scale, effectively alleviating the above problems. The fast bilateral segmentation network (FaBiNet) is used to segment images. It is a high-precision model with a detail branch that captures low-level information and a lightweight semantic branch that captures high-level semantic context. We used more than 80,000 osteosarcoma MRI images from three hospitals in China for detection, and the results showed that our model can achieve an accuracy of around 0.95 and a params of 2.33 M.

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          U-Net: Convolutional Networks for Biomedical Image Segmentation

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            Fully convolutional networks for semantic segmentation

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              Feature Pyramid Networks for Object Detection

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                Author and article information

                Contributors
                Journal
                Mathematics
                Mathematics
                MDPI AG
                2227-7390
                April 2022
                March 28 2022
                : 10
                : 7
                : 1090
                Article
                10.3390/math10071090
                8fa00357-986f-4343-afa6-e803cb635e54
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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