Detection and Labeling of Vertebrae in MR Images Using Deep Learning with Clinical Annotations as Training Data

By Daniel Forsberg, Erik Sjöblom, Jeffrey L. Sunshine. 2017

99 citations recorded by the source corpus.

Subfields: Biomedical Engineering, Oral Surgery, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Arvid Lundervold
2. Daguang Xu
3. B. van Ginneken
4. Mingxia Liu

In Oral Surgery

1. Arvid Lundervold

In Radiology, Nuclear Medicine and Imaging

1. B. van Ginneken
2. Arvid Lundervold
3. Francesco Ciompi
4. J. A. van der Laak
5. Geert J. S. Litjens
6. Daguang Xu
7. Mingxia Liu
8. A. Ahuja

Citations in the corpus

• An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “Vertebrae labeling [293] Designed a CNN for detection and labeling of vertebrae in MR images with clinical annotations as training data”
• A survey on deep learning in medical image analysis (2017, background): “(2016) MRI Hand age estimation; 2D regression CNN analyzes 13 bones Forsberg et al. (2017) MRI Vertebrae detection and labeling; outputs of two CNNs are input to graphical model Spampinato et al.”
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images (2020, methodology): “Forsberg et al. (2017) employ CNNs to detect the vertebrae followed by labelling them using graphical models.”