ArticlePloS one2017
A Novel Mouse Segmentation Method Based on Dynamic Contrast Enhanced Micro-CT Images.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Demystifying machine learning approaches in digital bone imaging using microCT and HRpQCT.Bone reports · 2026Review
- Optimizing SUV Analysis: A Multicenter Study on Preclinical FDG-PET/CT Highlights the Impact of Standardization.Molecular imaging and biology · 2024Observational
- Ultrahigh resolution whole body photon counting computed tomography as a novel versatile tool for translational research from mouse to man.Zeitschrift fur medizinische Physik · 2023Article
- 3D autofluorescence imaging of hydronephrosis and renal anatomical structure using cryo-micro-optical sectioning tomography.Theranostics · 2023Article
- Article
- Multimodality system of x-ray and fluorescence based on Fourier single-pixel imaging for small animals.Journal of biomedical optics · 2022Article
- Deep Learning-based Automatic Lung Segmentation on Multiresolution CT Scans from Healthy and Fibrotic Lungs in Mice.Radiology. Artificial intelligence · 2022Article
- LAMA: automated image analysis for the developmental phenotyping of mouse embryos.Development (Cambridge, England) · 2021Article
- Deep learning-enabled multi-organ segmentation in whole-body mouse scans.Nature communications · 2020Article
- Synthesis and in vivo evaluation of PEG-BP-BaYbFJournal of materials chemistry. B · 2020Article
- An interactive and intuitive visualisation method for X-ray computed tomography data of biological samples in 3D Portable Document Format.Scientific reports · 2019Article
- Automatic multiatlas based organ at risk segmentation in mice.The British journal of radiology · 2019Article
- Quantitative morphometric analysis of adult teleost fish by X-ray computed tomography.Scientific reports · 2018Article
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4 authors.
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Abstract
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.
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