Level set evolution with intensity prior knowledge for multiple sclerosis lesion segmentation

Zhaoxuan Gong, Wei Guo, Zhenyu Zhu, Jia Guo, Wei Li, Guodong Zhang
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Abstract

Background and Objectives: Multiple sclerosis (MS) lesion segmentation is important in estimating the progress of the disease and measuring the impact of new clinical treatments. Manual lesion delineation for the segmentation of lesions is time-consuming and suffers from observer variability. Therefore, a fully automated MS lesion segmentation method is considerable important in clinical practice. Subjects and Methods: In this study, we present a multilabel fusion embedded level set method for white matter lesion segmentation from MS patient images. Specifically, we focus on the validation of the variational level set method. Lesion segmentation is achieved by extending the level set contour which consists of an intensity-constrained term, an image data term, and a regularization term. Results: To compare the performance of our method with other state-of-the-art methods, we evaluated the methods with 25 magnetic resonance imaging datasets of MS patients. The dice score reaches an average of 0.55 for the proposed method. The sensitivity value and specificity value reach an average of 0.89 and 0.14, respectively. Conclusions: Experimental results demonstrate that our method is robust to parameter setting and outperforms other methods. The intensity-constrained term plays a key role in improving the segmentation accuracy. The experimental results show that our approach is effective and robust for lesion segmentation, which might simplify the quantification of lesions in basic research and even clinical trials.
基于强度先验知识的水平集进化用于多发性硬化症病灶分割
背景与目的:多发性硬化症(MS)病变分割对于估计疾病进展和衡量新的临床治疗效果具有重要意义。手工的病灶描绘对病灶的分割是费时的,并且受到观察者的变化。因此,一种全自动的MS病变分割方法在临床实践中具有十分重要的意义。研究对象和方法:在这项研究中,我们提出了一种多标签融合嵌入水平集方法,用于从MS患者图像中分割白质病变。具体来说,我们关注的是变分水平集方法的验证。通过扩展由强度约束项、图像数据项和正则化项组成的水平集轮廓来实现病灶分割。结果:为了与其他最先进的方法进行比较,我们用25个MS患者的磁共振成像数据集对方法进行了评估。对于所提出的方法,骰子得分达到0.55的平均值。灵敏度值和特异度值平均分别为0.89和0.14。结论:实验结果表明,该方法对参数设置具有鲁棒性,优于其他方法。强度约束项对提高分割精度起着关键作用。实验结果表明,该方法对病灶分割是有效的、鲁棒的,可以简化基础研究甚至临床试验中病灶的量化。
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