An automated slice sorting technique for multi-slice computed tomography liver cancer images using convolutional network

2021 ◽  
pp. 115686
Author(s):  
Amandeep Kaur ◽  
Ajay Pal Singh Chauhan ◽  
Ashwani Kumar Aggarwal
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Convolutional Network ◽  
Sorting Technique

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

2021 ◽  
Vol 20 ◽  
pp. 153303382110164
Author(s):  
Sang Bu An ◽  
Kwangmo Yang ◽  
Chang Won Kim ◽  
Si Ho Choi ◽  
Eunji Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Contrast Agents ◽  
Kupffer Cells ◽  
Micro Computed Tomography ◽  
Hepatic Parenchyma ◽  
Heterogeneous Distribution ◽  
Computed Tomography Images ◽  
Longitudinal Imaging ◽  
Over Time

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.

Non-Rigid Liver Registration in Liver Computed Tomography Images Using Elastic Method with Global and Local Deformations

2021 ◽  
Vol 11 (3) ◽  
pp. 810-816
Author(s):  
Taeyong Park ◽  
Jeongjin Lee ◽  
Juneseuk Shin ◽  
Kyoung Won Kim ◽  
Ho Chul Kang
Keyword(s):  
Computed Tomography ◽  
Liver Cancer ◽  
Ct Images ◽  
Local Deformation ◽  
Rigid Registration ◽  
Liver Ct ◽  
Liver Surface ◽  
Global And Local ◽  
Local Deformations

The study of follow-up liver computed tomography (CT) images is required for the early diagnosis and treatment evaluation of liver cancer. Although this requirement has been manually performed by doctors, the demands on computer-aided diagnosis are dramatically growing according to the increased amount of medical image data by the recent development of CT. However, conventional image segmentation, registration, and skeletonization methods cannot be directly applied to clinical data due to the characteristics of liver CT images varying largely by patients and contrast agents. In this paper, we propose non-rigid liver segmentation using elastic method with global and local deformation for follow-up liver CT images. To manage intensity differences between two scans, we extract the liver vessel and parenchyma in each scan. And our method binarizes the segmented liver parenchyma and vessel, and performs the registration to minimize the intensity difference between these binarized images of follow-up CT images. The global movements between follow-up CT images are corrected by rigid registration based on liver surface. The local deformations between follow-up CT images are modeled by non-rigid registration, which aligns images using non-rigid transformation, based on locally deformable model. Our method can model the global and local deformation between follow-up liver CT scans by considering the deformation of both the liver surface and vessel. In experimental results using twenty clinical datasets, our method matches the liver effectively between follow-up portal phase CT images, enabling the accurate assessment of the volume change of the liver cancer. The proposed registration method can be applied to the follow-up study of various organ diseases, including cardiovascular diseases and lung cancer.

scholarly journals Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

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.

Sign in / Sign up

Export Citation Format

Share Document