Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles

	Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles
	Li, Ke; Ye, Chuyang; Yang, Zhen; Carass, Aaron; Ying, Sarah; Prince, Jerry
	2016-03
会议名称	SPIE Medical Imaging
会议录名称	SPIE Medical Imaging
会议日期	2016-2
会议地点	San Diego, USA
摘要	Cerebellar peduncles (CPs) are white matter tracts connecting the cerebellum to other brain regions. Automatic segmentation methods of the CPs have been proposed for studying their structure and function. Usually the performance of these methods is evaluated by comparing segmentation results with manual delineations (ground truth). However, when a segmentation method is run on new data (for which no ground truth exists) it is highly desirable to efficiently detect and assess algorithm failures so that these cases can be excluded from scientific analysis. In this work, two outlier detection methods aimed to assess the performance of an automatic CP segmentation algorithm are presented. The first one is a univariate non-parametric method using a box-whisker plot. We first categorize automatic segmentation results of a dataset of diffusion tensor imaging (DTI) scans from 48 subjects as either a success or a failure. We then design three groups of features from the image data of nine categorized failures for failure detection. Results show that most of these features can efficiently detect the true failures. The second method—supervised classification—was employed on a larger DTI dataset of 249 manually categorized subjects. Four classifiers—linear discriminant analysis (LDA), logistic regression (LR), support vector machine (SVM), and random forest classification (RFC)—were trained using the designed features and evaluated using a leave-one-out cross validation. Results show that the LR performs worst among the four classifiers and the other three perform comparably, which demonstrates the feasibility of automatically detecting segmentation failures using classification methods.
关键词	Quality Assurance Segmentation Cerebellar Peduncles Outlier Detection Box-whisker Plot Classification
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12101
专题	脑图谱与类脑智能实验室_脑网络组研究
通讯作者	Li, Ke
推荐引用方式 GB/T 7714	Li, Ke,Ye, Chuyang,Yang, Zhen,et al. Quality assurance using outlier detection on an automatic segmentation method for the cerebellar peduncles[C],2016.

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