An Attention-based AI-assisted Segmentation System for Osteosarcoma MRI Images
Fangfang Gou, Jia Wu
Abstract
Osteosarcoma is one of the common malignant bone tumors, which is great harm to human health. Magnetic resonance imaging (MRI) is a common method for osteosarcoma detection because it can more clearly see the extent of lesions and the degree of invasion, with minimal damage to the human body. However, in developing countries or areas with less developed medical technology, due to the high cost of hardware equipment and the problem of image detection accuracy, the existing MRI image-assisted detection technology still has not achieved the expected application. Based on this, this study proposes an attention-based MRI image-assisted segmentation system for osteosarcoma (AIMSost). The results of the system segmentation can assist doctors to determine the tumor area and identify the tumor location. Among them, we design a segmentation method, which is based on the idea of AttendSeg. We design a new module to replace the original convolutional layer, and use residual network structure, LayerNorm, and Depth-Wise to reduce the number of parameters and operational complexity of the model. Experiments show that the DSC and IOU values of AIMSost are improved by more than 2.79% and 2.03%, respectively, compared with other models in the experiment. Our proposed method has not only higher segmentation accuracy, but also higher efficiency and a more lightweight model structure. The code for AIMSost is available online at https://github.com/gnafuog/AIMSost.git.