Automated tibiofemoral joint segmentation based on deeply supervised 2D-3D ensemble U-Net: Data from the Osteoarthritis Initiative

作者：

Highlights：

• We presented a robust automatic tibiofemoral joint segmentation framework based on deeply supervised 2D-3D ensemble U-Net

• Foreground oversampling is performed to combat class imbalance - one-third of the patch contains at least one foreground class

• Deep supervision (weighted auxiliary loss) is considered a form of regularization to alleviate the gradient vanishing problem

• The end-to-end framework provides an exceptional segmentation speed leadership (~ 62 seconds) per 3D volume

摘要

•We presented a robust automatic tibiofemoral joint segmentation framework based on deeply supervised 2D-3D ensemble U-Net•Foreground oversampling is performed to combat class imbalance - one-third of the patch contains at least one foreground class•Deep supervision (weighted auxiliary loss) is considered a form of regularization to alleviate the gradient vanishing problem•The end-to-end framework provides an exceptional segmentation speed leadership (~ 62 seconds) per 3D volume

论文关键词：Osteoarthritis,Tibiofemoral joint segmentation,CNN,Automated segmentation,Deep supervision,Deep learning

论文评审过程：Received 23 June 2021, Revised 7 November 2021, Accepted 8 November 2021, Available online 14 November 2021, Version of Record 16 November 2021.

论文官网地址：https://doi.org/10.1016/j.artmed.2021.102213