Multimodal Correlative Preclinical Whole Body Imaging and Segmentation

@article{AkselrodBallin2016MultimodalCP,
  title={Multimodal Correlative Preclinical Whole Body Imaging and Segmentation},
  author={Ayelet Akselrod-Ballin and Hagit Dafni and Yoseph Addadi and Inbal E. Biton and Reut Avni and Yafit Brenner and Michal Neeman},
  journal={Scientific Reports},
  year={2016},
  volume={6}
}
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… 

A preclinical micro-computed tomography database including 3D whole body organ segmentations

A database containing 225 murine 3D whole body μCT scans along with manual organ segmentation of most important organs including heart, liver, lung, trachea, spleen, kidneys, stomach, intestine, bladder, thigh muscle, bone, as well as subcutaneous tumors is introduced.

Deep learning-enabled multi-organ segmentation in whole-body mouse scans

A deep learning solution called AIMOS that automatically segments major organs and the skeleton in less than a second, orders of magnitude faster than prior algorithms and addresses the issue of human bias by identifying the regions where humans are most likely to disagree.

Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network

The accuracy and efficiency of mouse micro-CT image analysis are greatly improved and the need for using contrast agent and high x-ray dose is potentially reduced and that localized single organ prediction is more accurate than global multiple organ prediction.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models and can be used in parallel with the previously published method for muscle mass quantification.

Correlated Multimodal Imaging in Life Sciences: Expanding the Biomedical Horizon

A comprehensive overview of the field of CMI from preclinical hybrid imaging to correlative microscopy is presented, requirements for optimization and standardization are highlighted, and current efforts to bridge the gap between preclinical and biological imaging are focused on.

Kidney and Tumor Segmentation using U-Net Deep Learning Model

U-Net deep learning model was used for semantic segmentation in medical Image Segmentation for its suitability on small data set and also it was originally designed for Biomedical Image segmentation process.

Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging

Automated quantification of bioluminescence images

An analysis platform featuring an animal mold, a probabilistic organ atlas, and a mirror gantry to perform automatic in vivo bioluminescence quantification is presented, promising to increase data throughput and data reproducibility and accelerate human disease modeling in mice.

Digital Three-Dimensional Imaging Techniques Provide New Analytical Pathways for Malacological Research

Abstract: Research on molluscan specimens is increasingly being carried out using high-throughput molecular techniques. Due to their efficiency, these technologies have effectively resulted in a

References

SHOWING 1-10 OF 64 REFERENCES

Estimation of Mouse Organ Locations Through Registration of a Statistical Mouse Atlas With Micro-CT Images

An atlas-based approach for estimating the major organs in mouse micro-CT images and revealed that the statistical atlas has the advantage of improving the estimation of low-contrast organs.

Construction of an abdominal probabilistic atlas and its application in segmentation

By incorporating the atlas information into the Bayesian framework, segmentation results clearly showed improvements over a standard unsupervised segmentation method.

Interactive Local Super-Resolution Reconstruction of Whole-Body MRI Mouse Data: A Pilot Study with Applications to Bone and Kidney Metastases

It is shown that local SRR-MRI is a computationally efficient complementary imaging modality for the precise characterization of tumor metastases, and that the method provides a feasible high-resolution alternative to conventional MRI.

Organ approximation in μCT data with low soft tissue contrast using an articulated whole-body atlas

An approach for organ approximation in low contrast muCT data of mice using a whole-body mouse atlas is presented and the calculated dice indices of volume overlap show significant improvement compared to earlier studies.

Efficient Multilevel Brain Tumor Segmentation With Integrated Bayesian Model Classification

A Bayesian formulation for incorporating soft model assignments into the calculation of affinities, which are conventionally model free, and integrated into the multilevel segmentation by weighted aggregation algorithm for glioblastoma multiforme brain tumor.

A Survey of Current Methods in Medical Image Segmentation

A critical appraisal of the current status of semi-automated and automated methods for the segmentation of anatomical medical images is presented with an emphasis placed on revealing the advantages and disadvantages of these methods for medical imaging applications.

Articulated Whole-Body Atlases for Small Animal Image Analysis: Construction and Applications

The articulated atlas proves to be a useful tool for multimodality image combination, follow-up studies, and image processing in the scope of molecular imaging.

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

This paper proposes a non-rigid registration approach for the automatic registration of mouse whole body skeletons, which is based on the improved 3D shape context non- rigged registration method, and demonstrates the stability and accuracy of the proposed method.

An Integrated Segmentation and Classification Approach Applied to Multiple Sclerosis Analysis

A novel multiscale approach that combines segmentation with classification to detect abnormal brain structures in medical imagery is presented, and its utility in detecting multiple sclerosis lesions in 3D MRI data is demonstrated.
...