Bilayered anatomically constrained split-and-merge expectation maximisation algorithm (BiASM) for brain segmentation

TY - CHAP

T1 - Bilayered anatomically constrained split-and-merge expectation maximisation algorithm (BiASM) for brain segmentation

AU - Sudre, Carole H.

AU - Cardoso, M. Jorge

AU - Ourselin, Sébastien

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Dealing with pathological tissues is a very challenging task in medical brain segmentation. The presence of pathology can indeed bias the ultimate results when the model chosen is not appropriate and lead to missegmentations and errors in the model parameters. Model fit and segmentation accuracy are impaired by the lack of flexibility of the model used to represent the data. In this work, based on a finite Gaussian mixture model, we dynamically introduce extra degrees of freedom so that each anatomical tissue considered is modelled as a mixture of Gaussian components. The choice of the appropriate number of components per tissue class relies on a model selection criterion. Its purpose is to balance the complexity of the model with the quality of the model fit in order to avoid overfitting while allowing flexibility. The parameters optimisation, constrained with the additional knowledge brought by probabilistic anatomical atlases, follows the expectation maximisation (EM) framework. Split-and-merge operations bring the new flexibility to the model along with a data-driven adaptation. The proposed methodology appears to improve the segmentation when pathological tissue are present as well as the model fit when compared to an atlas-based expectation maximisation algorithm with a unique component per tissue class. These improvements in the modelling might bring new insight in the characterisation of pathological tissues as well as in the modelling of partial volume effect.

AB - Dealing with pathological tissues is a very challenging task in medical brain segmentation. The presence of pathology can indeed bias the ultimate results when the model chosen is not appropriate and lead to missegmentations and errors in the model parameters. Model fit and segmentation accuracy are impaired by the lack of flexibility of the model used to represent the data. In this work, based on a finite Gaussian mixture model, we dynamically introduce extra degrees of freedom so that each anatomical tissue considered is modelled as a mixture of Gaussian components. The choice of the appropriate number of components per tissue class relies on a model selection criterion. Its purpose is to balance the complexity of the model with the quality of the model fit in order to avoid overfitting while allowing flexibility. The parameters optimisation, constrained with the additional knowledge brought by probabilistic anatomical atlases, follows the expectation maximisation (EM) framework. Split-and-merge operations bring the new flexibility to the model along with a data-driven adaptation. The proposed methodology appears to improve the segmentation when pathological tissue are present as well as the model fit when compared to an atlas-based expectation maximisation algorithm with a unique component per tissue class. These improvements in the modelling might bring new insight in the characterisation of pathological tissues as well as in the modelling of partial volume effect.

KW - Expectation-Maximisation algorithm

KW - Gaussian mixture model

KW - Model selection

KW - Pathologies

KW - Segmentation

KW - Split-and-merge operations

UR - http://www.scopus.com/inward/record.url?scp=84902105379&partnerID=8YFLogxK

U2 - 10.1117/12.2041690

DO - 10.1117/12.2041690

M3 - Conference paper

AN - SCOPUS:84902105379

SN - 9780819498274

VL - 9034

BT - Medical Imaging 2014

PB - SPIE

T2 - Medical Imaging 2014: Image Processing

Y2 - 16 February 2014 through 18 February 2014

ER -