Joint Cranial Bone Labeling and Landmark Detection in Pediatric CT Images Using Context Encoding

Author:

Liu Jiawei1ORCID,Xing Fuyong1,Shaikh Abbas1ORCID,French Brooke2ORCID,Linguraru Marius George3,Porras Antonio R4ORCID

Affiliation:

1. Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO, USA

2. Department of Pediatric Plastic and Reconstructive Surgery, Children’s Hospital Colorado, Aurora, CO, USA

3. Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA

4. Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, USA,

Funder

National Institute of Dental and Craniofacial Research (NIDCR) of the National Institutes of Health

National Institute of Dental and Craniofacial Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Reference30 articles.

1. Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net

2. Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images

3. SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

4. U-Net: Convolutional networks for biomedical image segmentation;ronneberger;Medical Image Computing and Computer-Assisted Intervention—MICCAI,2015

5. 3D facial landark detection and face registration;perakis,2010

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