A non-rigid registration method for mouse whole body skeleton registration

Xiao, Di; Zahra, David; Bourgeat, Pierrick; Berghofer, Paula; Tamayo, Oscar Acosta; Wimberley, Catriona; Grégoire, Marie Claude; Salvado, Olivier

doi:10.1117/12.844067

Cited by 2 publications

(3 citation statements)

References 16 publications

(17 reference statements)

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“…Equivalent quantitative results were not available from other studies. ** Both tables reported results in terms of Dice except for Xiao et al 9. reporting results in terms of the mean absolute distance (MAD) error.…”

Section: Figurementioning

confidence: 99%

“…Similarly, in 6 7 8 an atlas based registration method was presented warping the digimouse atlas to a surface of optical imaging data. The authors in 9 proposed a non-rigid whole body skeleton registration for mice, based on 3D shape context model of point based surface registration.…”

Section: Segmentation Of Whole Body Small Animal Imagesmentioning

confidence: 99%

“… ** Both tables reported results in terms of Dice except for Xiao et al . 9 reporting results in terms of the mean absolute distance (MAD) error. …”

Section: Figurementioning

confidence: 99%

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Multimodal Correlative Preclinical Whole Body Imaging and Segmentation

Akselrod-Ballin

Dafni

Addadi

et al. 2016

Sci Rep

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Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging.

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Section: Figurementioning

confidence: 99%