Abstract:Micro-CT is widely used in preclinical studies of small animals. Due to the low soft-tissue contrast in typical studies, segmentation of soft tissue organs from noncontrast enhanced micro-CT images is a challenging problem. Here, we propose an atlas-based approach for estimating the major organs in mouse micro-CT images. A statistical atlas of major trunk organs was constructed based on 45 training subjects. The statistical shape model technique was used to include inter-subject anatomical variations. The shap… Show more
“…The DSC between manually segmented and interpolated organs varied between 0.470 ± 0.080 for the kidneys and 0.730 ± 0.040 for the brain, which is far lower than our accuracy. A statistical atlas based on 45 training subjects was constructed by Wang et al [15] and used to yield high accuracy registration. The lung and heart had a DSC as high as 0.900.…”
Section: Discussionmentioning
confidence: 99%
“…Similarly, a 3-D shape context-based nonrigid registration method was proposed to register skeleton, lung, and skin, and a thin-plate-spline (TPS) transformation was built to map all organs [14]. Wang et al [15] constructed a statistical atlas based on multiple training subjects to include inter-subject anatomical variations. Thus, the proposed statistical shape model and conditional Gaussian model-based image registration method yielded higher accuracy.…”
With the development of hybrid imaging scanners, micro-CT is widely used in locating abnormalities, studying drug metabolism, and providing structural priors to aid image reconstruction in functional imaging. Due to the low contrast of soft tissues, segmentation of soft tissue organs from mouse micro-CT images is a challenging problem. In this paper, we propose a mouse segmentation scheme based on dynamic contrast enhanced micro-CT images. With a homemade fast scanning micro-CT scanner, dynamic contrast enhanced images were acquired before and after injection of non-ionic iodinated contrast agents (iohexol). Then the feature vector of each voxel was extracted from the signal intensities at different time points. Based on these features, the heart, liver, spleen, lung, and kidney could be classified into different categories and extracted from separate categories by morphological processing. The bone structure was segmented using a thresholding method. Our method was validated on seven BALB/c mice using two different classifiers: a support vector machine classifier with a radial basis function kernel and a random forest classifier. The results were compared to manual segmentation, and the performance was assessed using the Dice similarity coefficient, false positive ratio, and false negative ratio. The results showed high accuracy with the Dice similarity coefficient ranging from 0.709 ± 0.078 for the spleen to 0.929 ± 0.006 for the kidney.
“…The DSC between manually segmented and interpolated organs varied between 0.470 ± 0.080 for the kidneys and 0.730 ± 0.040 for the brain, which is far lower than our accuracy. A statistical atlas based on 45 training subjects was constructed by Wang et al [15] and used to yield high accuracy registration. The lung and heart had a DSC as high as 0.900.…”
Section: Discussionmentioning
confidence: 99%
“…Similarly, a 3-D shape context-based nonrigid registration method was proposed to register skeleton, lung, and skin, and a thin-plate-spline (TPS) transformation was built to map all organs [14]. Wang et al [15] constructed a statistical atlas based on multiple training subjects to include inter-subject anatomical variations. Thus, the proposed statistical shape model and conditional Gaussian model-based image registration method yielded higher accuracy.…”
With the development of hybrid imaging scanners, micro-CT is widely used in locating abnormalities, studying drug metabolism, and providing structural priors to aid image reconstruction in functional imaging. Due to the low contrast of soft tissues, segmentation of soft tissue organs from mouse micro-CT images is a challenging problem. In this paper, we propose a mouse segmentation scheme based on dynamic contrast enhanced micro-CT images. With a homemade fast scanning micro-CT scanner, dynamic contrast enhanced images were acquired before and after injection of non-ionic iodinated contrast agents (iohexol). Then the feature vector of each voxel was extracted from the signal intensities at different time points. Based on these features, the heart, liver, spleen, lung, and kidney could be classified into different categories and extracted from separate categories by morphological processing. The bone structure was segmented using a thresholding method. Our method was validated on seven BALB/c mice using two different classifiers: a support vector machine classifier with a radial basis function kernel and a random forest classifier. The results were compared to manual segmentation, and the performance was assessed using the Dice similarity coefficient, false positive ratio, and false negative ratio. The results showed high accuracy with the Dice similarity coefficient ranging from 0.709 ± 0.078 for the spleen to 0.929 ± 0.006 for the kidney.
“…Prior studies have shown that a multisubject animal atlas promises to be a more realistic representation of an expected organ distribution than an atlas of a single-subject mouse atlas (Fig. 5d ) 38 , 42 . Fig.…”
We developed a computer-aided analysis tool for quantitatively determining bioluminescent reporter distributions inside small animals. The core innovations are a body-fitting animal shuttle and a statistical mouse atlas, both of which are spatially aligned and scaled according to the animal’s weight, and hence provide data congruency across animals of varying size and pose. In conjunction with a multispectral bioluminescence tomography technique capitalizing on the spatial framework of the shuttle, the in vivo biodistribution of luminescent reporters can rapidly be calculated and, thus, enables operator-independent and computer-driven data analysis. We demonstrate its functionality by quantitatively monitoring a bacterial infection, where the bacterial organ burden was determined and validated with the established serial-plating method. In addition, the statistical mouse atlas was validated and compared to existing techniques providing an anatomical reference. The proposed data analysis tool promises to increase data throughput and data reproducibility and accelerate human disease modeling in mice.
“…This approach was later applied by 13 for super-resolution reconstruction of MRI in whole-body mouse data in studies of tumor metastases. Recently 14 15 developed an automatic statistical atlas (multi-subject) approach to registration of micro-CT images in mice. The statistical atlas shape model registration first registers high contrast organs, and then estimates low contrast organs based on the first set of registered organs.…”
Section: Segmentation Of Whole Body Small Animal Imagesmentioning
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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