发表机构
Zhengzhou Police University; Beijing University of Posts and Telecommunications; Chinese Academy of Sciences; Qilu University of Technology; Jinan Supercomputing Center（郑州警察大学; 北京邮电大学; 中国科学院; 齐鲁工业大学; 济南超算中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在 CPU 皮肤镜分割中探究主动轮廓初始化与停止容差对评估的影响，发现 Otsu 初始化优于棋盘格但劣于阈值基线，强调评估需报告初始场、容差及迭代次数。
AI 中文摘要
轮廓初始化与数值停止条件可共同影响主动轮廓分割的评估结果。我们使用开源 scikit-image 库中的 Chan-Vese 实现，在调整尺寸后的 ISIC 2017 镜像数据集上检验了二者的交互作用。一个固定的 100 张图像开发集用于选择公共输入通道；随后对数据集中保留分区内的全部 600 张图像进行评估。在默认和更严格的水平集容差下，将 Otsu 阈值化与棋盘格、圆盘及 Otsu 初始化轮廓进行了比较。在默认容差下，相对于棋盘格初始化，Otsu 初始化使平均图像 Dice 系数从 0.6011 提升至 0.6660，配对差异为 0.0649（95% 图像自助法置信区间 [0.0452, 0.0860]）。单独使用 Otsu 阈值化达到 0.6897。默认圆盘初始化器在 471 张图像上仅迭代一次即停止。收紧容差后，Otsu 种子相对于棋盘格初始化的优势降至 0.0197，且大多数运行达到 500 次迭代上限。默认容差下的优势在小病灶与大病灶分层之间发生逆转。这些发现表明，相对于通用初始化器的改进可能与相对于阈值基线的性能下降并存。评估应保留未精炼的掩膜作为比较基准，并同时报告初始场定义、停止容差及观察到的迭代次数。
英文摘要
Contour initialization and numerical stopping can jointly affect the evaluation of active-contour segmentation. We examine their interaction using the open-source scikit-image Chan-Vese implementation on a resized ISIC 2017 mirror. A fixed development set of 100 images selects a common input channel; all 600 images in the repository's held-out partition are then evaluated. Otsu thresholding is compared with checkerboard-, disk-, and Otsu-initialized contours under default and tighter level-set tolerances. At the default tolerance, Otsu initialization increases mean image Dice from 0.6011 to 0.6660 relative to checkerboard initialization, a paired difference of 0.0649 (95% image-bootstrap interval [0.0452, 0.0860]). Otsu thresholding alone achieves 0.6897. The default disk initializer stops after one iteration on 471 images. Tightening the tolerance reduces the Otsu-seed advantage over checkerboard initialization to 0.0197, with most runs reaching the 500-iteration limit. The default-tolerance advantage also reverses between small- and large-lesion strata. These findings show that an improvement over a generic initializer can coexist with deterioration relative to the threshold baseline. Evaluations should retain the unrefined mask as a comparator and report the initial-field definition, stopping tolerance, and observed iteration counts together.
Comments10 pages, 3 figures, 2 tables
