A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries

Abstract

Among primary bone cancers, osteosarcoma is the most common, peaking between the ages of a child’s rapid bone growth and adolescence. The diagnosis of osteosarcoma requires observing the radiological appearance of the infected bones. A common approach is MRI, but the manual diagnosis of MRI images is prone to observer bias and inaccuracy and is rather time consuming. The MRI images of osteosarcoma contain semantic messages in several different resolutions, which are often ignored by current segmentation techniques, leading to low generalizability and accuracy. In the meantime, the boundaries between osteosarcoma and bones or other tissues are sometimes too ambiguous to separate, making it a challenging job for inexperienced doctors to draw a line between them. In this paper, we propose using a multiscale residual fusion network to handle the MRI images. We placed a novel subnetwork after the encoders to exchange information between the feature maps of different resolutions, to fuse the information they contain. The outputs are then directed to both the decoders and a shape flow block, used for improving the spatial accuracy of the segmentation map. We tested over 80,000 osteosarcoma MRI images from the PET-CT center of a well-known hospital in China. Our approach can significantly improve the effectiveness of the semantic segmentation of osteosarcoma images. Our method has higher F1, DSC, and IOU compared with other models while maintaining the number of parameters and FLOPS.

Artificial intelligence applications in pediatric oncology diagnosis

“…AI applications in the identification of diagnostic signature AI-based principles have been used for the detection and segmentation of pediatric malignant tumors. For example, Wu et al [64] used a residual fusion network to detect osteosarcomas on MRI scans; Peng et al [65] used a CNN for automated pediatric brain tumor detection and segmentation on MRI scans with automatic two-dimensional (2D) and volumetric size measurement of tumors; Strijbis et al [66] used a CNN for automated eye and tumor segmentation on MRI in retinoblastoma patients; and Bouget et al [67] used three-dimensional neural network architectures to automatically detect meningioma on MRI scans.…”

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

“…In addition, osteosarcoma itself has complex local tissue formation and morphological changes, difficult-to-maintain marginal features, and blurred tumor boundaries. Due to this, some medical image processing algorithms are less effective at identifying osteosarcomas, and it is challenging to obtain global multidirectional features and implicit features [ 20 ], as well as both accuracy and performance. It is crucial to improve the efficiency of osteosarcoma diagnosis by effectively extracting global features and solving the edge ambiguity segmentation problem without consuming too many computational resources and time costs.…”

“…It is crucial to improve the efficiency of osteosarcoma diagnosis by effectively extracting global features and solving the edge ambiguity segmentation problem without consuming too many computational resources and time costs. Edge feature-based methods are processing models employed in medical images, such as Transformer which has achieved wide application in the field of medical images by taking advantage of global modeling [ 20 , 21 , 22 ]. Such methods have achieved good results when dealing with simple tasks.…”

Image segmentation technology based on transformer in medical decision‐making system

“…Computer image recognition approaches enable the detection of tumour location and edges to a certain degree. Nevertheless, the lack of data sets and unclear images have led to a wide variation in the location and scale of tumour regions, which is worse for interpreting the network [28,29]. Therefore, designing segmentation models becomes one of the main aids to segment tumour regions from MRI images to support doctors in planning surgical procedures and making effective diagnoses.…”