Überatlas: Fast and robust registration for multi-atlas segmentation

Alvén, Jennifer; Norlén, Alexander; Enqvist, Olof; Kahl, Fredrik

doi:10.1016/j.patrec.2016.05.001

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…Let the notation μ is the operation of averaging, the feature of intensity is calculated as Equation (11). Assumed that the symbol x + u means the point where the distance from x is u, the Equation (12) shows the way to compute the feature of texture.…”

Section: Methods To Extract Featurementioning

confidence: 99%

“…The Atlas-based methods consider that the structures of the individual human brain are roughly the same and the label image provides the prior information for segmentation in the perspective of anatomy. [7][8][9][10][11][12][13][14] Hence, how to obtain the reliable prior knowledge, which is provided by the label image, is a fundamental to the atlas-based method.…”

Section: Introductionmentioning

confidence: 99%

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Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence‐weighted probability matrix

Liu

Song

et al. 2018

Int J Imaging Syst Tech

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The segmentation of specific tissues in an MR brain image for quantitative analysis can assist the disease diagnosis and medical research. Therefore, a robust and accurate method for automatic segmentation is necessary. Atlas‐based‐method is a common and effective method of automatic segmentation where an atlas refers to a pair of image consist of an intensity image and its corresponding label image. Apart from the general multi‐atlas‐based methods, which propagate labels through the single atlas then fuse them, we proposed a hybrid atlas forest based on confidence‐weighted probability matrix to consider the atlases set as a whole and treat each voxel differently. In the framework, we first register the atlas to the image space of target and calculate the confidence of voxels in the registered atlas. Then, a confidence‐weighted probability matrix is generated and it augments to the intensity image of the atlas or target for providing spatial information of the target tissue. Third, a hybrid atlas forest is trained to gather the features and correlation information among the atlases in the dataset. Finally, the segmentation of the target tissues is predicted by the trained hybrid atlas forest. The segment performance and the components efficiency of the proposed method are evaluated on the two public datasets. Based on the experiment results and quantitative comparisons, our method can gather spatial information and correlation among the atlases to obtain an accurate segmentation.

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Section: Methods To Extract Featurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence‐weighted probability matrix

Liu

Song

et al. 2018

Int J Imaging Syst Tech

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“…The landmark correspondences are computed through accurate non-rigid registrations between a randomly chosen reference atlas in the atlas set and each of the remaining atlases. The registration uses two channels, the image intensity and the ground truth mask of the organ of interest, and maximizes the similarity between these according to the normalized mutual information (NMI) measure using NiftyReg [1,24]. With the obtained displacement field, the mesh points of a triangular mesh of the ground truth surfaces are transformed to the coordinate system of the reference atlas.…”

Section: Establishing Golden Transformationsmentioning

confidence: 99%

Multiatlas Segmentation Using Robust Feature-Based Registration

Fejne

Landgren

Alvén

et al. 2017

Cloud-Based Benchmarking of Medical Image Analysis

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This paper presents a pipeline which uses a multiatlas approach for multiorgan segmentation in whole-body CT images. In order to obtain accurate registrations between the target and the atlas images, we develop an adapted featurebased method which uses organ-specific features. These features are learnt during an offline preprocessing step, and thus, the algorithm still benefits from the speed of feature-based registration methods. These feature sets are then used to obtain pairwise non-rigid transformations using RANSAC followed by a thin-plate spline refinement or NiftyReg. The fusion of the transferred atlas labels is performed using a random forest classifier, and finally, the segmentation is obtained using graph cuts with a Potts model as interaction term.

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“…Particularly, the atlas image registration component aligns atlas images and their segmentation labels to an image to be segmented, and then the aligned segmentation labels are fused to obtain a segmentation result for the image to be segmented by the atlas label component. Besides improving the image registration component (Hao et al, 2012b ; Alven et al, 2016 ; Doshi et al, 2016 ; Alchatzidis et al, 2017 ), many MAIS methods have focused on improving the label fusion component (Rohlfing et al, 2004 ; Coupé et al, 2011 ; Han, 2013 ; Wang et al, 2013 ; Hao et al, 2014 ; Amoroso et al, 2015 ; Roy et al, 2015 ; Wu et al, 2015 ; Zhu et al, 2015 , 2017 ; Doshi et al, 2016 ; Giraud et al, 2016 ; Zhang et al, 2017 ; Zu et al, 2017 ; Yang and Fan, 2018a , b ). In particular, majority voting (MV) might be the simplest label fusion method (Rohlfing et al, 2004 ), and more sophisticated label fusion strategies have been built upon a nonlocal patch-based label fusion strategy (Coupé et al, 2011 ), such as metric learning (Zhu et al, 2017 ), joint label fusion (Wang et al, 2013 ), and dictionary learning (Roy et al, 2015 ; Yang and Fan, 2018a ).…”

Section: Introductionmentioning

confidence: 99%

Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation

Zheng

Fan

2018

Front. Neuroinform.

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A novel label fusion method for multi-atlas based image segmentation method is developed by integrating semi-supervised and supervised machine learning techniques. Particularly, our method is developed in a pattern recognition based multi-atlas label fusion framework. We build random forests classification models for each image voxel to be segmented based on its corresponding image patches of atlas images that have been registered to the image to be segmented. The voxelwise random forests classification models are then applied to the image to be segmented to obtain a probabilistic segmentation map. Finally, a semi-supervised label propagation method is adapted to refine the probabilistic segmentation map by propagating its reliable voxelwise segmentation labels, taking into consideration consistency of local and global image appearance of the image to be segmented. The proposed method has been evaluated for segmenting the hippocampus in MR images and compared with alternative machine learning based multi-atlas based image segmentation methods. The experiment results have demonstrated that our method could obtain competitive segmentation performance (average Dice index > 0.88), compared with alternative multi-atlas based image segmentation methods under comparison. Source codes of the methods under comparison are publicly available at www.nitrc.org/frs/?group_id=1242.

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Überatlas: Fast and robust registration for multi-atlas segmentation

Cited by 15 publications

References 22 publications

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence‐weighted probability matrix

Automatic brain tissue segmentation in MR images using hybrid atlas forest based on confidence‐weighted probability matrix

Multiatlas Segmentation Using Robust Feature-Based Registration

Integrating Semi-supervised and Supervised Learning Methods for Label Fusion in Multi-Atlas Based Image Segmentation

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