1. Research question: With the growing interest in skin diseases and skin aesthetics, the ability to predict facial wrinkles is becoming increasingly important. This study aims to evaluate whether a computational model, convolutional neural networks (CNN), can be trained for automated facial wrinkle segmentation. 2. Findings: Our study presents an effective technique for integrating data from multiple annotators and illustrates that transfer learning can enhance performance, resulting in dependable segmentation of facial wrinkles. 3. Meaning: This approach automates intricate and time-consuming tasks of wrinkle analysis with a deep learning framework. It could be used to facilitate skin treatments and diagnostics.

翻译：1. 研究问题：随着对皮肤疾病和皮肤美学日益增长的兴趣，预测面部皱纹的能力变得愈发重要。本研究旨在评估卷积神经网络（CNN）能否被训练用于自动化面部皱纹分割。2. 研究发现：我们的研究提出了一种整合多标注者数据的有效技术，并证明了迁移学习能够提升性能，从而实现可靠的面部皱纹分割。3. 研究意义：该方法通过深度学习框架自动化了复杂且耗时的皱纹分析任务，可用于辅助皮肤治疗与诊断。