Probabilistic liver atlas construction

Fecha de publicación:

Autores de IIS La Fe

  • Estrella Durá Ferrandis

    Autor

  • Juan De Mata Domingo Esteve

    Autor

  • Guillermo Ayala Gallego

    Autor

Participantes ajenos a IIS La Fe

  • Goceri E

Grupos

Abstract

Background: Anatomical atlases are 3D volumes or shapes representing an organ or structure of the human body. They contain either the prototypical shape of the object of interest together with other shapes representing its statistical variations (statistical atlas) or a probability map of belonging to the object (probabilistic atlas). Probabilistic atlases are mostly built with simple estimations only involving the data at each spatial location. Results: A new method for probabilistic atlas construction that uses a generalized linear model is proposed. This method aims to improve the estimation of the probability to be covered by the liver. Furthermore, all methods to build an atlas involve previous coregistration of the sample of shapes available. The influence of the geometrical transformation adopted for registration in the quality of the final atlas has not been sufficiently investigated. The ability of an atlas to adapt to a new case is one of the most important quality criteria that should be taken into account. The presented experiments show that some methods for atlas construction are severely affected by the previous coregistration step. Conclusion: We show the good performance of the new approach. Furthermore, results suggest that extremely flexible registration methods are not always beneficial, since they can reduce the variability of the atlas and hence its ability to give sensible values of probability when used as an aid in segmentation of new cases.

Datos de la publicación

ISSN/ISSNe:
1475-925X, 1475-925X

Biomedical Engineering Online  BMC

Tipo:
Article
Páginas:
15-15
PubMed:
28086965
Factor de Impacto:
0,542 SCImago
Cuartil:
Q2 SCImago

Citas Recibidas en Web of Science: 11

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Keywords

  • Anatomical atlas; Probabilistic atlas; Generalized linear model; Coregistration method; Atlas variability

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