Longitudinal Imaging of Liver Cancer Using MicroCT and Nanoparticle Contrast Agents in CRISPR/Cas9-Induced Liver Cancer Mouse Model

Introduction: Micro-computed tomography with nanoparticle contrast agents may be a suitable tool for monitoring the time course of the development and progression of tumors. Here, we suggest a practical and convenient experimental method for generating and longitudinally imaging murine liver cancer models. Methods: Liver cancer was induced in 6 experimental mice by injecting clustered regularly interspaced short palindromic repeats/clustered regularly interspaced short palindromic repeats-associated protein 9 plasmids causing mutations in genes expressed by hepatocytes. Nanoparticle agents are captured by Kupffer cells and detected by micro-computed tomography, thereby enabling longitudinal imaging. A total of 9 mice were used for the experiment. Six mice were injected with both plasmids and contrast, 2 injected with contrast alone, and one not injected with either agent. Micro-computed tomography images were acquired every 2- up to 14-weeks after cancer induction. Results: Liver cancer was first detected by micro-computed tomography at 8 weeks. The mean value of hepatic parenchymal attenuation remained almost unchanged over time, although the standard deviation of attenuation, reflecting heterogeneous contrast enhancement of the hepatic parenchyma, increased slowly over time in all mice. Histopathologically, heterogeneous distribution and aggregation of Kupffer cells was more prominent in the experimental group than in the control group. Heterogeneous enhancement of hepatic parenchyma, which could cause image quality deterioration and image misinterpretation, was observed and could be due to variation in Kupffer cells distribution. Conclusion: Micro-computed tomography with nanoparticle contrast is useful in evaluating the induction and characteristics of liver cancer, determining appropriate size of liver cancer for testing, and confirming therapeutic response.

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Contours modeling of fascicular groups from micro‐ computed tomography images of peripheral nerves

Microscopy Research and Technique ◽

10.1002/jemt.23840 ◽

2021 ◽

Author(s):

Yingchun Zhong ◽

Jian Qi ◽

Fang Li ◽

Siyu Sun ◽

Shuang Zhu ◽

...

Keyword(s):

Computed Tomography ◽

Peripheral Nerves ◽

Micro Computed Tomography ◽

Computed Tomography Images

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A New Validation Methodology for In Silico Tools Based on X-ray Computed Tomography Images of Tablets and a Performance Analysis of One Tool

Pharmaceutics ◽

10.3390/pharmaceutics13091488 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1488

Author(s):

Sebastian Bollmann ◽

Peter Kleinebudde

Keyword(s):

Computed Tomography ◽

In Silico ◽

Pharmaceutical Formulations ◽

Prediction Errors ◽

Micro Computed Tomography ◽

Pharmaceutical Dosage Forms ◽

X Ray ◽

Computed Tomography Images ◽

X Ray Computed ◽

In Silico Tools

In silico tools which predict the dissolution of pharmaceutical dosage forms using virtual matrices can be validated with virtual matrices based on X-ray micro-computed tomography images of real pharmaceutical formulations. Final processed images of 3 different tablet batches were used to check the performance of the in silico tool F-CAD. The goal of this work was to prove the performance of the software by comparing the predicted dissolution profiles to the experimental ones and to check the feasibility and application of the validation concept for in silico tools. Both virtual matrices based on X-ray micro-computed tomography images and designed by the software itself were used. The resulting dissolution curves were compared regarding their similarity to the experimental curve. The kinetics were analysed with the Higuchi and Korsmeyers–Peppas plot. The whole validation concept as such was feasible and worked well. It was possible to identify prediction errors of the software F-CAD and issues with the virtual tablets designed within the software.

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Quantitative characterization of rodent feto-placental vasculature morphology in micro-computed tomography images

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2019.104984 ◽

2019 ◽

Vol 179 ◽

pp. 104984 ◽

Cited By ~ 2

Author(s):

Yutthapong Tongpob ◽

Shushan Xia ◽

Caitlin Wyrwoll ◽

Andrew Mehnert

Keyword(s):

Computed Tomography ◽

Micro Computed Tomography ◽

Quantitative Characterization ◽

Computed Tomography Images

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3D Morphological Processing for Wheat Spike Phenotypes Using Computed Tomography Images

Remote Sensing ◽

10.3390/rs11091110 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1110 ◽

Cited By ~ 3

Author(s):

Biao Xiong ◽

Bo Wang ◽

Shengwu Xiong ◽

Chengde Lin ◽

Xiaohui Yuan

Keyword(s):

Computed Tomography ◽

Aspect Ratio ◽

Human Performance ◽

Volume Growth ◽

Wheat Breeding ◽

Morphological Processing ◽

Micro Computed Tomography ◽

Computed Tomography Images ◽

Progressive Technology ◽

Wheat Spike

Wheat is the main food crop today world-wide. In order to improve its yields, researchers are committed to understand the relationships between wheat genotypes and phenotypes. Compared to progressive technology of wheat gene section identification, wheat trait measurement is mostly done manually in a destructive, labor-intensive and time-consuming way. Therefore, this study will be greatly accelerated and promoted if we can automatically discover wheat phenotype in a nondestructive and fast manner. In this paper, we propose a novel pipeline based on 3D morphological processing to detect wheat spike grains and stem nodes from 3D X-ray micro computed tomography (CT) images. We also introduce a set of newly defined 3D phenotypes, including grain aspect ratio, porosity, Grain-to-Grain distance, and grain angle, which are very difficult to be manually measured. The analysis of the associations among these traits would be very helpful for wheat breeding. Experimental results show that our method is able to count grains more accurately than normal human performance. By analyzing the relationships between traits and environment conditions, we find that the Grain-to-Grain distance, aspect ratio and porosity are more likely affected by the genome than environment (only tested temperature and water conditions). We also find that close grains will inhibit grain volume growth and that the aspect ratio 3.5 may be the best for higher yield in wheat breeding.

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Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

10.21203/rs.3.rs-65573/v1 ◽

2020 ◽

Author(s):

Yodit Abebe Ayalew ◽

Kinde Anlay Fante ◽

Mohammed Aliy

Keyword(s):

Computed Tomography ◽

Deep Learning ◽

Liver Cancer ◽

Ct Scan ◽

Tumor Segmentation ◽

Cancer Death ◽

Liver Segmentation ◽

Abdominal Ct ◽

Computed Tomography Images ◽

Abdominal Ct Scan

Abstract Background: Liver cancer is the sixth most common cancer worldwide. According to WHO data in 2017, the liver cancer death in Ethiopia reached 1040 (0.16%) from all cancer deaths. Hepatocellular carcinoma (HCC), primary liver cancer causes the death of around 700,000 people each year worldwide and this makes it the third leading cause of cancer death. HCC is occurred due to cirrhosis and hepatitis B or C viruses. Liver cancer mostly diagnosed with a computed tomography (CT) scan. But, the detection of the tumor from the CT scan image is difficult since tumors have similar intensity with nearby tissues and may have a different appearance depending on their type, state, and equipment setting. Nowadays deep learning methods have been used for the segmentation of liver and its tumor from the CT scan images and they are more efficient than those traditional methods. But, they are computationally expensive and need many labeled samples for training, which are difficult in the case of biomedical images. Results: A deep learning-based segmentation algorithm is employed for liver and tumor segmentation from abdominal CT scan images. Three separate UNet models, one for liver segmentation and the others two for tumor segmentation from the segmented liver and directly from the abdominal CT scan image were used. A dice score of 0.96 was obtained for liver segmentation. And a dice score of 0.74 and 0.63 was obtained for segmentation of tumor from the liver and from abdominal CT scan image respectively. Conclusion: The research improves the liver tumor segmentation that will help the physicians in the diagnosis and detection of liver tumors and in designing a treatment plan for the patient. And for the patient, it increases the patients’ chance of getting treatment and decrease the mortality rate due to liver cancer.

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