The segmentation of cancer-suspicious skin lesions using ultrasound may help their differential diagnosis and treatment planning. Active contour models (ACM) require an initial seed, which when manually chosen may cause variations in segmentation accuracy. Fully-automated skin segmentation typically employs layer-by-layer segmentation using a combination of methods; however, such segmentation has not yet been applied on cancerous lesions. In the current work, fully automated segmentation is achieved in two steps: an automated seeding (AS) step using a layer-by-layer method followed by a growing step using an ACM. The method was tested on images of nevi, melanomas, and basal cell carcinomas from two ultrasound imaging systems (N=60), with all lesions being successfully located. For the seeding step, manual seeding (MS) was used as a reference. AS approached the accuracy of MS when the latter used an optimal bounding rectangle based on the ground truth (Sørensen-Dice coefficient (SDC) of 72.3 vs 74.6, respectively). The effect of varying the manual seed was also investigated; a 0.7 decrease in seed height and width caused a mean SDC of 54.6. The results show the robustness of automated seeding for skin lesion segmentation.

中文翻译：

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用于超声皮肤病变分割的自动播种

使用超声波对癌症可疑皮肤病变的分割可能有助于他们的鉴别诊断和治疗计划。活动轮廓模型 (ACM) 需要初始种子，手动选择时可能会导致分割精度发生变化。全自动皮肤分割通常采用方法组合的逐层分割；然而，这种分割尚未应用于癌性病变。在当前的工作中，全自动分割分两步实现：使用逐层方法的自动播种 (AS) 步骤，然后是使用 ACM 的生长步骤。该方法在来自两个超声成像系统 (N=60) 的痣、黑色素瘤和基底细胞癌的图像上进行了测试，所有病变都被成功定位。对于接种步骤，使用手动接种 (MS) 作为参考。当 MS 使用基于真实情况的最佳边界矩形（Sørensen-Dice 系数 (SDC) 分别为 72.3 和 74.6）时，AS 接近 MS 的准确性。还研究了改变人工种子的影响；种子高度和宽度减少 0.7 导致平均 SDC 为 54.6。结果表明自动播种对皮肤病变分割的鲁棒性。