A label fusion method using conditional random fields with higher-order potentials: Application to hippocampal segmentation.
Objective: The objective of this study is to develop a probabilistic modeling framework for segmenting structures of interest from a collection of atlases. We present a label fusion method that is based on minimizing an energy function using graph-cut techniques.Methods and Materials: We use a condi...
| Publicado en: | Artificial Intelligence in Medicine Vol. 64; no. 2; pp. 117 - 130 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jun2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109748203&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109748203 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Jun2015 vid: 64 iid: 2 pid: 1004 pub: Elsevier B.V. artinfo: ui: 109748203 NLM25982908 2013048191 10.1016/j.artmed.2015.04.005 NLM25982908 109748203 ppf: 117 ppct: 13 formats: tig: atl: A label fusion method using conditional random fields with higher-order potentials: Application to hippocampal segmentation. aug: au: Platero, Carlos Carmen Tobar, M sug: ab: Objective: The objective of this study is to develop a probabilistic modeling framework for segmenting structures of interest from a collection of atlases. We present a label fusion method that is based on minimizing an energy function using graph-cut techniques.Methods and Materials: We use a conditional random field (CRF) model that allows us to efficiently incorporate shape, appearance and context information. This model is characterized by a pseudo-Boolean function defined on unary, pairwise and higher-order potentials. Given a subset of registered atlases in the target image for a particular region of interest (ROI), we first derive an appearance-shape model from these registered atlases. The unary potentials combine an appearance model based on multiple features with a label prior using a weighted voting method. The pairwise terms are defined from a Finsler metric that minimizes the surface of separation between voxels whose labels are different. The higher-order potentials used in our framework are based on the robust P(n) model proposed by Kohli et al. The higher-order potentials enforce label consistency in cliques; hence, the proposed method can be viewed as an approach to integrate high-level information with images based on low-level features. To evaluate the performance and the robustness of the proposed label fusion method, we employ two available databases of T1-weighted (T1W) magnetic resonance (MR) images of human brains. We compare our approach with other label fusion methods in the automatic hippocampal segmentation from T1W-MR images.Results: Our label fusion method yields mean Dice coefficients of 0.829 and 0.790 for the two databases used with mean times of approximately 80 and 160s, respectively.Conclusions: We introduce a new label fusion method based on a CRF model and on ROIs. The CRF model is characterized by a pseudo-Boolean function defined on unary, pairwise and higher-order potentials. The proposed Boolean function is representable by graphs. A globally optimal binary labeling is found using a st-mincut algorithm in each ROI. We show that the proposed approach is very competitive with respect to recently reported methods. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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