A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies
A subtraction pipeline for automatic detection of new appearing multiple sclerosis lesions in longitudinal studies
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DOI：
10.1007/s00234-014-1343-1
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发表时间：
2014-05-01
期刊：
影响因子：
2.8
通讯作者：
Llado, Xavier
中科院分区：
文献类型：
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作者：
Ganiler, Onur;Oliver, Arnau;Llado, Xavier
磁共振成像(MRI)的时间序列分析对多发性硬化(MS)的诊断和随访具有重要价值。本文提出了一种结合多序列信息的无监督减影方法来处理纵向研究中新的MS病变的检测。提出的检测新病变的流水线包括以下步骤：颅骨剥离、偏场校正、直方图匹配、配准、白质掩蔽、图像减影、自动阈值和后处理。我们还结合PD-W和T2-W图像的结果来减少假阳性检测。在20名MS患者中进行了实验测试，两次时间间隔为12个月(12个月)或48个月(48个月)。对于12M和48M的数据集，该管道获得了非常好的性能，总体灵敏度分别为0.83和0.77，错误发现率(FDR)分别为0.14和0.18。对于管道来说，最困难的情况是检测非常小的病变，获得的灵敏度较低，而FDR较高。我们的全自动方法强大而准确，允许检测新出现的MS病变。我们相信，该流水线可以应用于大型图像收集，也可以很容易地适应于监测其他大脑病理。
Time-series analysis of magnetic resonance images (MRI) is of great value for multiple sclerosis (MS) diagnosis and follow-up. In this paper, we present an unsupervised subtraction approach which incorporates multisequence information to deal with the detection of new MS lesions in longitudinal studies.The proposed pipeline for detecting new lesions consists of the following steps: skull stripping, bias field correction, histogram matching, registration, white matter masking, image subtraction, automated thresholding, and postprocessing. We also combine the results of PD-w and T2-w images to reduce false positive detections.Experimental tests are performed in 20 MS patients with two temporal studies separated 12 (12M) or 48 (48M) months in time. The pipeline achieves very good performance obtaining an overall sensitivity of 0.83 and 0.77 with a false discovery rate (FDR) of 0.14 and 0.18 for the 12M and 48M datasets, respectively. The most difficult situation for the pipeline is the detection of very small lesions where the obtained sensitivity is lower and the FDR higher.Our fully automated approach is robust and accurate, allowing detection of new appearing MS lesions. We believe that the pipeline can be applied to large collections of images and also be easily adapted to monitor other brain pathologies.