Jha Abhinav K, Kupinski Matthew A, Rodríguez Jeffrey J, Stephen Renu M, Stopeck Alison T
College of Optical Sciences, University of Arizona, Tucson, Arizona.
Proc SPIE Int Soc Opt Eng. 2010 Feb 27;7627. doi: 10.1117/12.845515.
Apparent Diffusion Coefficient (ADC) of lesions obtained from Diffusion Weighted Magnetic Resonance Imaging is an emerging biomarker for evaluating anti-cancer therapy response. To compute the lesion's ADC, accurate lesion segmentation must be performed. To quantitatively compare these lesion segmentation algorithms, standard methods are used currently. However, the end task from these images is accurate ADC estimation, and these standard methods don't evaluate the segmentation algorithms on this task-based measure. Moreover, standard methods rely on the highly unlikely scenario of there being perfectly manually segmented lesions. In this paper, we present two methods for quantitatively comparing segmentation algorithms on the above task-based measure; the first method compares them given good manual segmentations from a radiologist, the second compares them even in absence of good manual segmentations.
从扩散加权磁共振成像获得的病变表观扩散系数（ADC）是一种用于评估抗癌治疗反应的新兴生物标志物。为了计算病变的ADC，必须进行准确的病变分割。为了定量比较这些病变分割算法，目前使用标准方法。然而，这些图像的最终任务是准确的ADC估计，而这些标准方法并未基于此任务指标评估分割算法。此外，标准方法依赖于存在完美手动分割病变这种极不可能的情况。在本文中，我们提出了两种基于上述任务指标定量比较分割算法的方法；第一种方法在有放射科医生提供的良好手动分割的情况下对算法进行比较，第二种方法即使在没有良好手动分割的情况下也能对算法进行比较。
