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Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach.

Gill G, Toews M, Beichel RR - Int J Biomed Imaging (2014)

Bottom Line: The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features.The mean absolute surface distance error was 0.948 ± 1.537 mm.The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA ; The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA.

ABSTRACT
Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.

No MeSH data available.


Related in: MedlinePlus

Example of representative lung features ρr. Each column shows a single feature in axial and frontal view.
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fig2: Example of representative lung features ρr. Each column shows a single feature in axial and frontal view.

Mentions: Steps (b)–(d) are repeated until convergence, that is, when the Frobenius norm of the transformation matrix difference max⁡//Tit − Tit−1// is zero [18]. This yields a representative set of lung features ρr = fr (Figure 2). Note that the iterative group-wise alignment procedure reduces the dependency on the selection of the reference image [18].


Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans: A Feature-Based Atlas Approach.

Gill G, Toews M, Beichel RR - Int J Biomed Imaging (2014)

Example of representative lung features ρr. Each column shows a single feature in axial and frontal view.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4221988&req=5

fig2: Example of representative lung features ρr. Each column shows a single feature in axial and frontal view.
Mentions: Steps (b)–(d) are repeated until convergence, that is, when the Frobenius norm of the transformation matrix difference max⁡//Tit − Tit−1// is zero [18]. This yields a representative set of lung features ρr = fr (Figure 2). Note that the iterative group-wise alignment procedure reduces the dependency on the selection of the reference image [18].

Bottom Line: The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features.The mean absolute surface distance error was 0.948 ± 1.537 mm.The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA ; The Iowa Institute for Biomedical Imaging, The University of Iowa, Iowa City, IA 52242, USA.

ABSTRACT
Model-based segmentation methods have the advantage of incorporating a priori shape information into the segmentation process but suffer from the drawback that the model must be initialized sufficiently close to the target. We propose a novel approach for initializing an active shape model (ASM) and apply it to 3D lung segmentation in CT scans. Our method constructs an atlas consisting of a set of representative lung features and an average lung shape. The ASM pose parameters are found by transforming the average lung shape based on an affine transform computed from matching features between the new image and representative lung features. Our evaluation on a diverse set of 190 images showed an average dice coefficient of 0.746 ± 0.068 for initialization and 0.974 ± 0.017 for subsequent segmentation, based on an independent reference standard. The mean absolute surface distance error was 0.948 ± 1.537 mm. The initialization as well as segmentation results showed a statistically significant improvement compared to four other approaches. The proposed initialization method can be generalized to other applications employing ASM-based segmentation.

No MeSH data available.


Related in: MedlinePlus