Work

Anatomic-landmark detection using graphical context modelling

Year: 2015

Type: conference-paper

Abstract: Anatomical landmarks in images play an important role in medical practice. This paper presents a graphical model that fully automatically detects such landmarks. The model includes a unary potential using a random forest classifier based on local appearance and binary and ternary potentials encoding geometrical context among different landmarks. The weightings of different potentials are learned in a maximum likelihood manner. The final detection result is formulated as the maximum-a-posteriori estimation jointly over the whole set of landmarks in one image. For validation, the model is applied to detect right-ventricle insert points in cardiac MR images. The result shows that the context modelling is able to substantially improve the overall accuracy.

Source: IEEE International Symposium on Biomedical Imaging (ISBI)

DOI: 10.1109/isbi.2015.7164114

Authors: Lichao Wang, Vasileios Belagiannis, Carsten Marr, Fabian Theis, Guang-Zhong Yang, Nassir Navab

Institutions: Helmholtz Munich, Institute of Computational Biology, Technical University of Munich, Imperial College London

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W1557230635

FWCI: 0.134

Cites: 14

Cited by: 1

Related to: 10