The BoneXpert Method for Automated Determination of Skeletal Maturity

By H. H. Thodberg, S. Kreiborg, A. Juul, K. D. Pedersen. IEEE Transactions on Medical Imaging, 2009

427 citations recorded by the source corpus.

Subfields: Archeology, Geometry and Topology, Oral Surgery

View Paper: https://www.semanticscholar.org/paper/79124c4e7d6066206acf468c04a6f340b940fe16

How researchers see this paper

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

In Archeology

1. Vladimir I. Iglovikov
2. Alexey A. Shvets
3. Alexandr A Kalinin
4. Alexander Rakhlin

In Geometry and Topology

1. Alexander Rakhlin

Citations in the corpus

• Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks (2017, methodology): “Only recently software solutions, such as BoneXpert [24], have been developed and approved for the clinical use in Europe.”
• Deep learning for automated skeletal bone age assessment in X‐ray images (2017, methodology): “While most of the above methods are based on the TW method, recently, Thodberg et al. (2009) proposed BoneXpert, which performs automatic age assessment through a unified model of TW and G&P methods.”
• Fully Automated Deep Learning System for Bone Age Assessment (2017, methodology): “The most successful attempt to date is BoneXpert [33], a software only medical device approved for use in Europe and the first commercial implementation of automated BAA. utilizes a generative model, the active appearance model (AAM), to automatically segment 15 bones in the hand and wrist and then ”