Accurate lymphoma segmentation on Positron Emission Tomography (PET) images is of great importance for medical diagnoses, such as for distinguishing benign and malignant. To this end, this paper proposes an adaptive weighting and scalable distance regularized level set evolution (AW-SDRLSE) method for delineating lymphoma boundaries on 2D PET slices. There are three important characteristics with respect to AW-SDRLSE: 1) A scalable distance regularization term is proposed and a parameter $q$ can control the contour's convergence rate and precision in theory. 2) A novel dynamic annular mask is proposed to calculate mean intensities of local interior and exterior regions and further define the region energy term. 3) As the level set method is sensitive to parameters, we thus propose an adaptive weighting strategy for the length and area energy terms using local region intensity and boundary direction information. AW-SDRLSE is evaluated on 90 cases of real PET data with a mean Dice coefficient of 0.8796. Comparative results demonstrate the accuracy and robustness of AW-SDRLSE as well as its performance advantages as compared with related level set methods. In addition, experimental results indicate that AW-SDRLSE can be a fine segmentation method for improving the lymphoma segmentation results obtained by deep learning (DL) methods significantly.

中文翻译：

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AW-SDRLSE：PET 图像淋巴瘤分割的自适应加权和可缩放距离正则化水平集演化

在正电子发射断层扫描 (PET) 图像上准确分割淋巴瘤对于医学诊断非常重要，例如区分良性和恶性。为此，本文提出了一种自适应加权和可缩放距离正则化水平集演化 (AW-SDRLSE) 方法，用于在 2D PET 切片上描绘淋巴瘤边界。AW-SDRLSE 具有三个重要特征：1）提出了一个可扩展的距离正则化项和一个参数$q$理论上可以控制轮廓的收敛速度和精度。2）提出了一种新颖的动态环形掩模来计算局部内部和外部区域的平均强度并进一步定义区域能量项。3）由于水平集方法对参数敏感，因此我们提出了一种使用局部区域强度​​和边界方向信息的长度和面积能量项的自适应加权策略。AW-SDRLSE 在 90 例真实 PET 数据上进行评估，平均 Dice 系数为 0.8796。比较结果证明了 AW-SDRLSE 的准确性和鲁棒性，以及与相关水平集方法相比的性能优势。此外，