Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.
Medical image analysis methods have been applied to clinical scenarios of tumor diagnosis and treatment. Many studies have attempted to optimize the effectiveness of tumor MRI image segmentation by deep learning, but they do not consider the optimization of local details and the interaction of global semantic information. Second, although medical image pattern recognition can learn representative semantic features, it is challenging to ignore useless features in order to learn generalizable embeddings. Thus, a tumor-assisted segmentation method is proposed to detect tumor lesion regions and boundaries with complex shapes. Specifically, we introduce a denoising convolutional autoencoder (DCAE) for MRI image noise reduction. Furthermore, we design a novel tumor MRI image segmentation framework (NFSR-U-Net) based on class-correlation pattern aggregation, which first aggregates class-correlation patterns in MRI images to form a class-correlational representation. Then the relationship of similar class features is identified to closely correlate the dense representations of local features for classification, which is conducive to identifying image data with high heterogeneity. Meanwhile, the model uses a spatial attention mechanism and residual structure to extract effective information of the spatial dimension and enhance statistical information in MRI images, which bridges the semantic gap in skip connections. In the study, over 4000 MRI images from the Monash University Research Center for Artificial Intelligence are analyzed. The results show that the method achieves segmentation accuracy of up to 96% for tumor MRI images with low resource consumption.
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