论文信息 - Applying simultaneous super-resolution and contrast synthesis to routine clinical magnetic resonance images for automated segmentation of knee joint cartilage

Applying simultaneous super-resolution and contrast synthesis to routine clinical magnetic resonance images for automated segmentation of knee joint cartilage

Ales Neubert∗1 ales.neubert@csiro.au Pierrick Bourgeat∗1 pierrick.bourgeat@csiro.au Jason Wood1 jason.wood@csiro.au Craig Engstrom2 c.engstrom@uq.edu.au Shekhar S. Chandra2 shekhar.chandra@uq.edu.au Stuart Crozier2 stuart@itee.uq.edu.au Jurgen Fripp1 jurgen.fripp@csiro.au 1 The Australian e-Health Research Centre, CSIRO Health and Biosecurity, Herston, Australia 2 School of Information Technology and Electrical Engineering, The University of Queensland, St Lucia, Australia

[1] Stefan Zachow,et al. Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative , 2019, Medical Image Anal..

[2] Jeff Wood,et al. Super‐resolution musculoskeletal MRI using deep learning , 2018, Magnetic resonance in medicine.

[3] Richard Kijowski,et al. Deep convolutional neural network for segmentation of knee joint anatomy , 2018, Magnetic resonance in medicine.

[4] Stuart Crozier,et al. Automatic segmentation of the bone and extraction of the bone–cartilage interface from magnetic resonance images of the knee , 2007, Physics in medicine and biology.

[5] Thomas Brox,et al. U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.