Automated segmentation and area estimation of neural foramina with boundary regression model
作者：
Highlights：
• Application: it firstly achieved a fully automated and multi-modal segmentation tool for clinical diagnosis of NFS.
• Approach: it creatively formulated segmentation as boundary regression to leverage machine learning in a holistic way.
• Methodology: it proposed a highly nonlinear multi-kernel multi-output SVR to seamlessly combine MSVR and MKL together.
摘要
Highlights•Application: it firstly achieved a fully automated and multi-modal segmentation tool for clinical diagnosis of NFS.•Approach: it creatively formulated segmentation as boundary regression to leverage machine learning in a holistic way.•Methodology: it proposed a highly nonlinear multi-kernel multi-output SVR to seamlessly combine MSVR and MKL together.
论文关键词：Automated segmentation,Area estimation,Neural foramina stenosis,Boundary regression model,Multiple output support vector regression,Multiple kernel learning
论文评审过程：Received 29 December 2015, Revised 5 September 2016, Accepted 21 September 2016, Available online 25 September 2016, Version of Record 27 November 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.018
