Accurate Segmentation of Lumbar Spine Magnetic Resonance Imaging by Deep Neural Network with Size-Adaptive Loss

Author:

Zhang Yingdi1ORCID,Shi Zelin1ORCID,Wang Huan2ORCID,Yan Chongnan2ORCID,Wang Lanbo2ORCID,Mu Yueming2ORCID,Liu Yunpeng1ORCID,Wu Shuhang1ORCID,Liu Tianci1ORCID,Pang Mingqi1ORCID

Affiliation:

1. Shenyang Institute of Automation, Chinese Academy of Sciences, China

2. Spine Surgery Department, ShengJing Hospital of China Medical University, China

Publisher

ACM

Reference16 articles.

1. Lim , Y.S. , Mun , J.-U. , Seo , M.S. , Sang , B.-H. , Bang , Y.-S. , Kang , K.N. , Koh , J.W. , Kim , Y.U. : Dural sac area is a more sensitive parameter for evaluating lumbar spinal stenosis than spinal canal area: A retrospective study. Medicine 96(49) ( 2017 ) Lim, Y.S., Mun, J.-U., Seo, M.S., Sang, B.-H., Bang, Y.-S., Kang, K.N., Koh, J.W., Kim, Y.U.: Dural sac area is a more sensitive parameter for evaluating lumbar spinal stenosis than spinal canal area: A retrospective study. Medicine 96(49) (2017)

2. Random sample consensus

3. Cross-sectional area of human trunk paraspinal muscles before and after posterior lumbar surgery using magnetic resonance imaging

4. LumNet: A Deep Neural Network for Lumbar Paraspinal Muscles Segmentation

5. Population-averaged MRI atlases for automated image processing and assessments of lumbar paraspinal muscles

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