Skeleton-based cerebrovascular quantitative analysis

骨架(计算机编程) 医学
作者
Xingce Wang,Enhui Liu,Zhongke Wu,Feifei Zhai,Yicheng Zhu,Wuyang Shui,Mingquan Zhou
出处
期刊:BMC Medical Imaging [BioMed Central]
卷期号:16 (1): 68- 被引量:5
标识
DOI:10.1186/s12880-016-0170-8
摘要

Cerebrovascular disease is the most common cause of death worldwide, with millions of deaths annually. Interest is increasing toward understanding the geometric factors that influence cerebrovascular diseases, such as stroke. Cerebrovascular shape analyses are essential for the diagnosis and pathological identification of these conditions. The current study aimed to provide a stable and consistent methodology for quantitative Circle of Willis (CoW) analysis and to identify geometric changes in this structure. An entire pipeline was designed with emphasis on automating each step. The stochastic segmentation was improved and volumetric data were obtained. The L1 medial axis method was applied to vessel volumetric data, which yielded a discrete skeleton dataset. A B-spline curve was used to fit the skeleton, and geometric values were proposed for a one-dimensional skeleton and radius. The calculations used to derive these values were illustrated in detail. In one example(No. 47 in the open dataset) all values for different branches of CoW were calculated. The anterior communicating artery(ACo) was the shortest vessel, with a length of 2.6mm. The range of the curvature of all vessels was (0.3, 0.9) ± (0.1, 1.4). The range of the torsion was (−12.4,0.8) ± (0, 48.7). The mean radius value range was (3.1, 1.5) ± (0.1, 0.7) mm, and the mean angle value range was (2.2, 2.9) ± (0, 0.2) mm. In addition to the torsion variance values in a few vessels, the variance values of all vessel characteristics remained near 1. The distribution of the radii of symmetrical posterior cerebral artery(PCA) and angle values of the symmetrical posterior communicating arteries(PCo) demonstrated a certain correlation between the corresponding values of symmetrical vessels on the CoW. The data verified the stability of our methodology. Our method was appropriate for the analysis of large medical image datasets derived from the automated pipeline for populations. This method was applicable to other tubular organs, such as the large intestine and bile duct.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
拼搏的沁发布了新的文献求助10
刚刚
Picky完成签到,获得积分10
刚刚
刚刚
1秒前
likes完成签到,获得积分10
1秒前
独特的平卉完成签到,获得积分10
2秒前
所所应助EL采纳,获得10
3秒前
斯文败类应助平常的樱桃采纳,获得10
3秒前
冯不言发布了新的文献求助10
4秒前
研友_VZG7GZ应助江直树附体采纳,获得10
6秒前
书剑飞侠完成签到,获得积分10
7秒前
SciGPT应助健康的珩采纳,获得10
10秒前
有点意思完成签到,获得积分10
11秒前
科研通AI6.1应助Rainyin采纳,获得50
11秒前
充电宝应助nihaoaaaa采纳,获得10
12秒前
黑米粥完成签到,获得积分10
12秒前
星辰大海应助喜剧人物采纳,获得10
12秒前
13秒前
大椒完成签到 ,获得积分10
14秒前
14秒前
QQ发布了新的文献求助10
14秒前
14秒前
15秒前
黑米粥发布了新的文献求助10
16秒前
霍碧完成签到,获得积分10
16秒前
寒水发布了新的文献求助10
16秒前
呆呆完成签到,获得积分10
16秒前
FashionBoy应助拼搏的沁采纳,获得10
16秒前
童77完成签到 ,获得积分10
17秒前
bruseli完成签到 ,获得积分10
18秒前
zzz完成签到,获得积分10
18秒前
tmw发布了新的文献求助20
19秒前
21秒前
nihaoaaaa发布了新的文献求助10
21秒前
wangyao_sir发布了新的文献求助10
22秒前
追寻逍遥发布了新的文献求助10
22秒前
zuozuo完成签到,获得积分10
23秒前
26秒前
夏雪儿完成签到,获得积分10
26秒前
诺曦完成签到,获得积分10
27秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Cronologia da história de Macau 5000
Merrill's Atlas of Radiographic Positioning and Procedures - 3-Volume Set, 16th Edition 2000
Matrix Methods in Data Mining and Pattern Recognition 540
Interactions of Vowel Quality and Prosody in East Slavic 500
Vander's Renal Physiology第10版 500
Materials Informatics Molecules, Crystals and Beyond A volume in Acta Materialia Book Series 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7057000
求助须知:如何正确求助?哪些是违规求助? 8720517
关于积分的说明 18460998
捐赠科研通 6579513
什么是DOI,文献DOI怎么找? 3122379
关于科研通互助平台的介绍 2213489
邀请新用户注册赠送积分活动 2097955