Dataset Information

Segmentation and visualization of a large, high-resolution micro-CT data of mice.

ABSTRACT: High-resolution large datasets were acquired to improve the understanding of murine bone physiology. The purpose of this work is to present the challenges and solutions in segmenting and visualizing bone in such large datasets acquired using micro-CT scan of mice. The analyzed dataset is more than 50 GB in size with more than 6,000 2,048?×?2,048 slices. The study was performed to automatically measure the bone mineral density (BMD) of the entire skeleton. A global Renyi entropy (GREP) method was initially used for bone segmentation. This method consistently oversegmented skeletal region. A new method called adaptive local Renyi entropy (ALREP) is proposed to improve the segmentation results. To study the efficacy of the ALREP, manual segmentation was performed. Finally, a specialized high-end remote visualization system along with the software, VirtualGL, was used to perform remote rendering of this large dataset. It was determined that GREP overestimated the bone cross-section by around 30 % compared with ALREP. The manual segmentation process took 6,300 min for 6,300 slices while ALREP took only 150 min for segmentation. Automatic image processing with ALREP method may facilitate BMD measurement of the entire skeleton in a significantly reduced time, compared with manual process.

SUBMITTER: Chityala R

PROVIDER: S-EPMC3597961 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Segmentation and visualization of a large, high-resolution micro-CT data of mice.

Chityala Ravishankar R Pudipeddi Sridevi S Arensten Luke L Hui Susanta S

Journal of digital imaging 20130401 2

High-resolution large datasets were acquired to improve the understanding of murine bone physiology. The purpose of this work is to present the challenges and solutions in segmenting and visualizing bone in such large datasets acquired using micro-CT scan of mice. The analyzed dataset is more than 50 GB in size with more than 6,000 2,048 × 2,048 slices. The study was performed to automatically measure the bone mineral density (BMD) of the entire skeleton. A global Renyi entropy (GREP) method was ...[more]

PMID: 22766797

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Project description:BACKGROUND:Micro-CT holds promising potential for phenotyping and histological purposes. However, few have clarified the difference in the neuroimaging quality between ex vivo and in vivo micro-CT scanners. In addition, no direct comparison has been made between micro-CT scans and standard microscopy. Furthermore, while the efficacy of various stains for yielding soft-tissue contrast in CT scans have been compared in other studies for embryos, staining protocols for larger samples have yet to be clarified. Lastly, post-acquisition processing for image enhancements have not been addressed. METHODS:Comparisons of postnatal rat brain micro-CT scans obtained through custom-built ex vivo and commercially available in vivo micro-CT scanners were made. Subsequently, the scanned rat brains were then H&E stained for microscopy. Neuroanatomy on micro-CT scanning and 4× microscopy of rat brain were compared. Diffusion and perfusion staining using iodine or PTA were trialled on adult and neonatal encapsulated rat brains. Different combinations of stain concentration and staining time were trialled. Post-acquisition denoising with NLM filter was completed using a modern General-Purpose Graphic Processing Unit (GPGPU) and custom code for prompt processing. RESULTS:Ex vivo micro-CT scans of iodine-stained postnatal rat brains yields 3D images with details comparable to 4× H&E light micrographs. Neural features shown on ex vivo micro-CT scans were significantly more distinctive than those on in vivo micro-CT scans. Both ex vivo and in vivo micro-CT scans required diffusion staining through small craniotomy. Perfusion staining is ineffective. Iodine staining was more efficient than PTA in terms of time. Consistently, enhancement made by NLM denoising on in vivo micro-CT images were more pronounced than that on ex vivo micro-CT scans due to their difference in image signal-to-noise indexes. CONCLUSIONS:Micro-CT scanning is a powerful and versatile visualization tool available for qualitative and potential quantitative anatomical analysis. Simple diffusion staining via craniotomy with 1.5% iodine is an effective and minimal structural-invasive method for both in vivo and ex vivo micro-CT scanning for studying the microscopic morphology of neonatal and adult rat brains. Post-acquisition NLM filtering is an effective enhancement technique for in vivo micro-CT brain scans.

Project description:OBJECTIVES:This study was conducted in order to compare the effect of field of view (FOV) size on image quality between ultra-high-resolution CT (U-HRCT) and conventional high-resolution CT (HRCT). METHODS:Eleven cadaveric lungs were scanned with U-HRCT and conventional HRCT and reconstructed with five FOVs (40, 80, 160, 240, and 320 mm). Three radiologists evaluated and scored the images. Three image evaluations were performed, comparing the image quality with the five FOVs with respect to the 160-mm FOV. The first evaluation was performed on conventional HRCT images, and the second evaluation on U-HRCT images. Images were scored on normal structure, abnormal findings, and overall image quality. The third evaluation was a comparison of the images obtained with conventional HRCT and U-HRCT, with scoring performed on overall image quality. Quantitative evaluation of noise was performed by setting ROIs. RESULTS:In conventional HRCT, image quality was improved when the FOV was reduced to 160 mm. In U-HRCT, image quality, except for noise, improved when the FOV was reduced to 80 mm. In the third evaluation, overall image quality was improved in U-HRCT over conventional HRCT at all FOVs. Noise of U-HRCT increased with respect to conventional HRCT when the FOV was reduced from 160 to 40 mm. However, at 240- and 320-mm FOVs, the noise of U-HRCT and conventional HRCT showed no differences. CONCLUSIONS:In conventional HRCT, image quality did not improve when the FOV was reduced below 160 mm. However, in U-HRCT, image quality improved even when the FOV was reduced to 80 mm. KEY POINTS:• Reducing the size of the field of view to 160 mm improves diagnostic imaging quality in high-resolution CT. • In ultra-high-resolution CT, improvements in image quality can be obtained by reducing the size of the field of view to 80 mm. • Ultra-high-resolution CT produces images of higher quality compared with conventional HRCT irrespective of the size of the field of view.

Dataset Information

Segmentation and visualization of a large, high-resolution micro-CT data of mice.

Publications

Segmentation and visualization of a large, high-resolution micro-CT data of mice.

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