Research and implementation of spinal MRI image segmentation algorithm

2016 ◽  
Author(s):  
Shuai Li ◽  
Yue Du ◽  
Hao-chun Liu
Keyword(s):  
Image Segmentation ◽  
Segmentation Algorithm ◽  
Spinal Mri ◽  
Image Segmentation Algorithm ◽  
Mri Image

scholarly journals Automatic Segmentation Algorithm of Magnetic Resonance Image in Diagnosis of Liver Cancer Patients under Deep Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Jinling Zhang ◽  
Jun Yang ◽  
Min Zhao
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Magnetic Resonance ◽  
Liver Cancer ◽  
Convolutional Neural Network ◽  
Cancer Patients ◽  
Segmentation Algorithm ◽  
Image Segmentation Algorithm ◽  
Single Sequence ◽  
Mri Image

To study the influence of different sequences of magnetic resonance imaging (MRI) images on the segmentation of hepatocellular carcinoma (HCC) lesions, the U-Net was improved. Moreover, deep fusion network (DFN), data enhancement strategy, and random data (RD) strategy were introduced, and a multisequence MRI image segmentation algorithm based on DFN was proposed. The segmentation experiments of single-sequence MRI image and multisequence MRI image were designed, and the segmentation result of single-sequence MRI image was compared with those of convolutional neural network (FCN) algorithm. In addition, RD experiment and single-input experiment were also designed. It was found that the sensitivity (0.595 ± 0.145) and DSC (0.587 ± 0.113) obtained by improved U-Net were significantly higher than the sensitivity (0.405 ± 0.098) and DSC (0.468 ± 0.115, P < 0.05 ) obtained by U-Net. The sensitivity of multisequence MRI image segmentation algorithm based on DFN (0.779 ± 0.015) was significantly higher than that of FCN algorithm (0.604 ± 0.056, P < 0.05 ). The multisequence MRI image segmentation algorithm based on the DFN had higher indicators for liver cancer lesions than those of the improved U-Net. When RD was added, it not only increased the DSC of the single-sequence network enhanced by the hepatocyte-specific magnetic resonance contrast agent (Gd-EOB-DTPA) by 1% but also increased the DSC of the multisequence MRI image segmentation algorithm based on DFN by 7.6%. In short, the improved U-Net can significantly improve the recognition rate of small lesions in liver cancer patients. The addition of RD strategy improved the segmentation indicators of liver cancer lesions of the DFN and can fuse image features of multiple sequences, thereby improving the accuracy of lesion segmentation.

scholarly journals The Value of Convolutional Neural Network-Based Magnetic Resonance Imaging Image Segmentation Algorithm to Guide Targeted Controlled Release of Doxorubicin Nanopreparation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Hujun Liu ◽  
Hui Gao ◽  
Fei Jia
Keyword(s):  
Image Segmentation ◽  
Controlled Release ◽  
Liver Cancer ◽  
Cancer Patients ◽  
Segmentation Algorithm ◽  
Resonance Imaging ◽  
Targeted Drug ◽  
3D Cnn ◽  
Image Segmentation Algorithm ◽  
Mri Image

There was an investigation of the auxiliary role of convolutional neural network- (CNN-) based magnetic resonance imaging (MRI) image segmentation algorithm in MRI image-guided targeted drug therapy of doxorubicin nanomaterials so that the value of drug-controlled release in liver cancer patients was evaluated. In this study, 80 patients with liver cancer were selected as the research objects. It was hoped that the CNN-based MRI image segmentation algorithm could be applied to the guided analysis of MRI images of the targeted controlled release of doxorubicin nanopreparation to analyze the imaging analysis effect of this algorithm on the targeted treatment of liver cancer with doxorubicin nanopreparation. The results of this study showed that the upgraded three-dimensional (3D) CNN-based MRI image segmentation had a better effect compared with the traditional CNN-based MRI image segmentation, with significant improvement in indicators such as accuracy, precision, sensitivity, and specificity, and the differences were all statistically marked ( p < 0.05 ). In the monitoring of the targeted drug therapy of doxorubicin nanopreparation for liver cancer patients, it was found that the MRI images of liver cancer patients processed by 3D CNN-based MRI image segmentation neural algorithm could be observed more intuitively and guided to accurately reach the target of liver cancer. The accuracy of targeted release determination of nanopreparation reached 80 ± 6.25%, which was higher markedly than that of the control group (66.6 ± 5.32%) ( p < 0.05 ). In a word, the MRI image segmentation algorithm based on CNN had good application potential in guiding patients with liver cancer for targeted therapy with doxorubicin nanopreparation, which was worth promoting in the adjuvant treatment of targeted drugs for cancer.

scholarly journals Expression Level of IL-17 in Peripheral Blood of Patients with Late Pregnancy and Diagnosis of Maternal-Fetal Tolerance Based on Brain MRI Image Segmentation Algorithm

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Zenying Yu ◽  
Shengyan Zhou ◽  
Zhen Tan ◽  
Guangmin Lu
Keyword(s):  
Image Segmentation ◽  
Peripheral Blood ◽  
Brain Mri ◽  
Late Pregnancy ◽  
Treg Cells ◽  
Severe Preeclampsia ◽  
Segmentation Algorithm ◽  
Reversible Posterior Encephalopathy Syndrome ◽  
Image Segmentation Algorithm ◽  
Mri Image

Objectives: To study the expression of IL-17 in peripheral blood and its effect on maternal-fetal tolerance in patients with eclampsia in late pregnancy using MRI image segmentation algorithm. Methods: Thirty-nine patients with severe preeclampsia and eclampsia with brain symptoms were examined by cranial MRI. Pregnant women with 32 weeks of pregnancy were selected to detect the percentage of Th17 and Treg cells in CD4 + T lymphocytes and the expression of cytokines IL-17 and IL-10 in peripheral blood. Results: MRI examination was normal in 26 cases, 9 cases showed reversible posterior encephalopathy syndrome, three cases were cerebral hemorrhage, and one case was intracranial cavernous sinus thrombosis. two. Compared with the mild preeclampsia group, the relative number of Thl7 cells increased and that of Treg cells decreased in the severe preeclampsia group (P>0.05). Conclusion: The major types of cerebrovascular diseases (CVD) in severe preeclampsia and eclampsia were reversible posterior encephalopathy syndrome and cerebral hemorrhage. It was speculated that the damage to the blood-brain barrier may play an important role in the pathogenesis. The balance of the number of Th17 cells/the number of Treg cells was more inclined to the Th17 cell-mediated pro-inflammatory state, Treg cell-mediated immune tolerance decreases, and it becomes more obvious with the worsening of the disease. doi: https://doi.org/10.12669/pjms.37.6-WIT.4828 How to cite this:Yu Z, Zhou S, Tan Z, Lu G. Expression Level of IL-17 in Peripheral Blood of Patients with Late Pregnancy and Diagnosis of Maternal-Fetal Tolerance Based on Brain MRI Image Segmentation Algorithm. Pak J Med Sci. 2021;37(6):1553-1557.  doi: https://doi.org/10.12669/pjms.37.6-WIT.4828 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Automatic lumbar spinal MRI image segmentation with a multi-scale attention network

2021 ◽  
Author(s):  
Haixing Li ◽  
Haibo Luo ◽  
Wang Huan ◽  
Zelin Shi ◽  
Chongnan Yan ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Spinal Mri ◽  
Lumbar Spinal ◽  
Mri Image

scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

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