期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Segmentation of carotid arterial walls using neural networks
1
作者 Daniel D Samber Sarayu RamachANDran +4 位作者 Anoop Sahota Sonum Naidu alison pruzan Zahi A Fayad Venkatesh Mani 《World Journal of Radiology》 CAS 2020年第1期1-9,共9页
BACKGROUND Automated,accurate,objective,and quantitative medical image segmentation has remained a challenging goal in computer science since its inception.This study applies the technique of convolutional neural netw... BACKGROUND Automated,accurate,objective,and quantitative medical image segmentation has remained a challenging goal in computer science since its inception.This study applies the technique of convolutional neural networks(CNNs)to the task of segmenting carotid arteries to aid in the assessment of pathology.AIM To investigate CNN’s utility as an ancillary tool for researchers who require accurate segmentation of carotid vessels.METHODS An expert reader delineated vessel wall boundaries on 4422 axial T2-weighted magnetic resonance images of bilateral carotid arteries from 189 subjects with clinically evident atherosclerotic disease.A portion of this dataset was used to train two CNNs(one to segment the vessel lumen and the other to segment the vessel wall)with the remaining portion used to test the algorithm’s efficacy by comparing CNN segmented images with those of an expert reader.Overall quantitative assessment between automated and manual segmentations was determined by computing the DICE coefficient for each pair of segmented images in the test dataset for each CNN applied.The average DICE coefficient for the test dataset(CNN segmentations compared to expert’s segmentations)was 0.96 for the lumen and 0.87 for the vessel wall.Pearson correlation values and the intra-class correlation coefficient(ICC)were computed for the lumen(Pearson=0.98,ICC=0.98)and vessel wall(Pearson=0.88,ICC=0.86)segmentations.Bland-Altman plots of area measurements for the CNN and expert readers indicate good agreement with a mean bias of 1%-8%.CONCLUSION Although the technique produces reasonable results that are on par with expert human assessments,our application requires human supervision and monitoring to ensure consistent results.We intend to deploy this algorithm as part of a software platform to lessen researchers’workload to more quickly obtain reliable results. 展开更多
关键词 Carotid arteries SEGMENTATION Convolutional neural network Magnetic resonance imaging Vessel wall
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部