Accuracy of an nnUNet neural network for the automatic segmentation of intracranial aneurysms, their parent vessels and major cerebral arteries from magnetic resonance imaging-Time of flight (MRI-TOF)

医学 磁共振成像 分割 动脉瘤 放射科 核医学 人工智能 计算机科学
作者
Elisa Colombo,Mathijs de Boer,Lambertus W. Bartels,Luca Regli,Tristan P. C. van Doormaal
出处
期刊:American Journal of Neuroradiology [American Society of Neuroradiology]
卷期号:: ajnr.A8607-ajnr.A8607
标识
DOI:10.3174/ajnr.a8607
摘要

ABSTRACT

BACKGROUND AND PURPOSE:

To develop a new machine-learning algorithm for fully automatic identification of cerebral arteries and intracranial aneurysms (IAs) based on a manually segmented magnetic resonance imaging with time-of-flight sequences (MRITOF) dataset.

MATERIALS AND METHODS:

In this retrospective single-center study, 62 MRI-TOF scans of a total of 73 untreated unruptured IAs were manually color-labelled in 21 classes. A nnUNet architecture was trained on MRI-TOF images. The performance of the automatic segmentation was compared with the manual segmentation using Dice Similarity Coefficient (DSC), Centerline Dice (ClDice) and 95th percentile Hausdorff Distance (HD95). Sensitivity was computed for aneurysm detection.

RESULTS:

Across all 21 classes, the median DSC was 0.86 [95CI: 0.81, 0.89], the median ClDice 0.91 [0.85, 0.94] and the median HD95 was 2.9 [1.0, 14.9] mm. Sensitivity of the model for aneurysms detection was 0.8. For this class specifically, a median DSC of 0.88 [0.13, 0.92], median ClDice of 0.89 [0.06, 1.0] and median HD95 of 1.8 [0.58, 81] mm was achieved. The volume of the labelled anatomical structure was the most relevant determinant of accuracy in this model. Median time to predict was 130.6 [60.9, 284.1] seconds.

CONCLUSIONS:

The nnUNet MRI-TOF based algorithm provided a fast and adequate automatic extraction of unruptured intracranial aneurysms, their parent vessels and the most relevant cerebral arteries. Future steps involve the expansion of the training set with the inclusion of more MRI-TOF studies with and without IAs and its incorporation in 3D imaging viewers and treatment prediction. ABBREVIATIONS: IA = Intracranial Aneurysm; MRI-TOF= Magnetic Resonance Imaging – Time of Flight; DSC = Dice-Sørenson Coefficient; ClDice = Centerline Dice; HD95 = 95th Percentile Hausdorff Distance.
最长约 10秒,即可获得该文献文件

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
佳佳佳发布了新的文献求助10
1秒前
1秒前
6z1aaaaa完成签到,获得积分10
1秒前
tmxx完成签到,获得积分10
1秒前
科目三应助不灭采纳,获得10
2秒前
寒冷麦片发布了新的文献求助10
2秒前
英俊的铭应助渺渺采纳,获得10
3秒前
4秒前
tmxx发布了新的文献求助10
4秒前
5秒前
6秒前
旧旧完成签到 ,获得积分10
6秒前
大模型应助大胆棒球采纳,获得10
7秒前
xio关闭了xio文献求助
7秒前
朝闻道发布了新的文献求助10
8秒前
8秒前
ZhouYW应助玩命的谷槐采纳,获得10
8秒前
9秒前
星辰大海应助花椒鱼采纳,获得10
9秒前
幽默的依秋完成签到,获得积分10
9秒前
9秒前
10秒前
11秒前
感动老师发布了新的文献求助10
11秒前
牛牛完成签到,获得积分10
11秒前
无花果应助你走以后采纳,获得10
12秒前
13秒前
13秒前
无花果应助guibihuantai001采纳,获得10
14秒前
寒冷麦片完成签到,获得积分20
14秒前
who发布了新的文献求助10
15秒前
15秒前
落后的亦寒完成签到,获得积分20
15秒前
15秒前
Passion完成签到,获得积分10
16秒前
Erina完成签到 ,获得积分10
16秒前
16秒前
苹果蜗牛完成签到 ,获得积分10
17秒前
小可发布了新的文献求助10
18秒前
aefs发布了新的文献求助10
18秒前
高分求助中
Les Mantodea de Guyane Insecta, Polyneoptera 2500
Mobilization, center-periphery structures and nation-building 600
Technologies supporting mass customization of apparel: A pilot project 450
China—Art—Modernity: A Critical Introduction to Chinese Visual Expression from the Beginning of the Twentieth Century to the Present Day 430
Tip60 complex regulates eggshell formation and oviposition in the white-backed planthopper, providing effective targets for pest control 400
A Field Guide to the Amphibians and Reptiles of Madagascar - Frank Glaw and Miguel Vences - 3rd Edition 400
China Gadabouts: New Frontiers of Humanitarian Nursing, 1941–51 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3791657
求助须知:如何正确求助?哪些是违规求助? 3336027
关于积分的说明 10278555
捐赠科研通 3052666
什么是DOI,文献DOI怎么找? 1675260
邀请新用户注册赠送积分活动 803270
科研通“疑难数据库(出版商)”最低求助积分说明 761165