Convolutional Neural Network for Segmentation and Measurement of Intima Media Thickness
| Autor: | Sunder T, C. Rajasekaran, Jayanthi K B, Nirmala Madian, Sudha S |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Computer science
Carotid Artery Common Medicine (miscellaneous) Health Informatics 02 engineering and technology Convolutional neural network Carotid Intima-Media Thickness Mean difference 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Health Information Management Right Common Carotid Artery Region of interest medicine.artery 0202 electrical engineering electronic engineering information engineering medicine Humans Segmentation cardiovascular diseases Common carotid artery business.industry Deep learning Pattern recognition Carotid Arteries Intima-media thickness cardiovascular system 020201 artificial intelligence & image processing Artificial intelligence Neural Networks Computer business Algorithms Information Systems |
| Zdroj: | Journal of medical systems. 42(8) |
| ISSN: | 1573-689X |
| Popis: | The measurement of Carotid Intima Media Thickness (IMT) on Common Carotid Artery (CCA) is a principle marker of risk of cardiovascular disease. This paper presents a novel method of using deep Convolutional Neural Network (CNN) for identification and measurement of IMT on the far wall of the artery. The Region of Interest (ROI) is extracted using CNN architecture with 8 layers. 110 subjects are taken for the study. Each subject is recorded with one Right Common Carotid Artery (RCCA) and Left Common Carotid Artery (LCCA) frame resulting in 220 recordings. Patch based segmentation with 2640 patches are given to the training network for ROI localization. Intima Media Complex (IMC) is the area where IMT is measured. This region is extracted after defining the ROI. Keeping in mind the end objective of measurement of IMT values binary threshold with snake algorithm is applied to extract the lumen-intima and media-adventitia boundary. IMT values are measured for 20 cases and mean difference is found to be 0.08 mm. |
| Databáze: | OpenAIRE |
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