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

Yedong Shen, Fangfang Gou and Zhehao Dai
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Yedong Shen: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Fangfang Gou: School of Computer Science and Engineering, Central South University, Changsha 410083, China
Zhehao Dai: Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha 410011, China

Mathematics, 2022, vol. 10, issue 7, 1-21

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.

Keywords: osteosarcoma; MRI image segmentation; artificial intelligence; mean teacher; auxiliary diagnosis; lightweight segmentation model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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