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SEGMA：一种使用滑动窗口和随机森林的人脑磁共振成像自动分割方法。

SEGMA: An Automatic SEGMentation Approach for Human Brain MRI Using Sliding Window and Random Forests.

作者信息

Serag Ahmed, Wilkinson Alastair G, Telford Emma J, Pataky Rozalia, Sparrow Sarah A, Anblagan Devasuda, Macnaught Gillian, Semple Scott I, Boardman James P

机构信息

MRC Centre for Reproductive Health, University of Edinburgh Edinburgh, UK.

Department of Radiology, Royal Hospital for Sick Children Edinburgh, UK.

出版信息

Front Neuroinform. 2017 Jan 20;11:2. doi: 10.3389/fninf.2017.00002. eCollection 2017.

DOI:10.3389/fninf.2017.00002
PMID:28163680
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5247463/
Abstract

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38-42 weeks gestational age), children and adolescents (4-17 years) and adults (35-71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course.

摘要

在整个生命过程中获取的脑磁共振成像（MRI）定量体积，可能有助于研究脑发育和健康衰老的风险及复原力因素的长期影响，以及理解成人大脑结构的早期生命决定因素。因此，对可应用于不同生命阶段获取图像的自动分割工具的需求日益增加。我们开发了一种用于人类脑MRI的自动分割方法，其中将滑动窗口方法和多类随机森林分类器应用于高维特征向量以进行精确分割。该方法在从179名个体获取的脑MRI数据上表现良好，这些个体被分为三个年龄组进行分析：新生儿（孕龄38 - 42周）、儿童和青少年（4 - 17岁）以及成年人（35 - 71岁）。由于该方法可以从部分标记的数据集中学习，它可用于高效分割大规模数据集。它还可应用于整个生命过程中的不同人群和成像模态。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce63/5247463/25c9ea752a25/fninf-11-00002-g0007.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce63/5247463/d8e40f8e9a8d/fninf-11-00002-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce63/5247463/86791b263524/fninf-11-00002-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce63/5247463/60d34a7ba4b4/fninf-11-00002-g0003.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ce63/5247463/25c9ea752a25/fninf-11-00002-g0007.jpg

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