Bone metastasis segmentation based on Improved U-NET algorithm

Abstract

Whole body bone scan image analysis is widely used in nuclear medicine to assist nuclear medicine physicians in the detection of bone metastases. At present, the analysis of whole-body bone scan images mainly relies on the manual reading of nuclear medicine doctors. The doctors, based on personal knowledge and experience, look for abnormal lesion locations and diagnose them by examining the whole-body bone scan images. However, this method is prone to misdiagnosis and missed diagnosis. To solve the above problems, this study proposes an image segmentation method based on deep learning, which can automatically identify the location of bone metastases, so that doctors can make more accurate diagnosis. The Methods Attention mechanism was added to the jump connection of the original U-NET network to enhance the image feature selection. Experiments show that the algorithm in this study teaches traditional U-Net to show better results on the three indicators of MIoU Dice and MAP.