AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of the AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (i.e., AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to the low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512 × 512 pixels were extracted using translation lesion boxes that encompassed lesions in different positions and captured different contexts of perilesional skin. In total, 16,488 translation-augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-Net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving upon both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.
光化性角化病（AK）是一种常见的皮肤癌前病变，其有效检测与治疗监测至关重要。为提升临床环境中AK病灶负荷监测的自动化程度与精确性，本研究评估了基于U-Net架构的语义分割模型（即AKU-Net）的应用效果。该模型采用迁移学习策略以弥补标注图像数据集规模有限的不足，并通过引入基于卷积长短期记忆网络（convLSTM）的循环处理机制，有效利用上下文信息，以应对AK病灶区域对比度低、边界模糊的识别挑战。研究使用包含115名光化性角化病患者共569张临床照片的标注数据集进行模型训练与评估。通过平移病灶框从每张图像中提取512×512像素的局部区域，这些区域覆盖不同位置的病灶并捕捉病灶周围皮肤的多样背景信息。最终使用16,488个经平移增强的局部图像进行模型训练，并采用403个病灶中心区域图像进行测试。为验证AKU-Net在病灶检测方面的改进效果，本研究将其与原始U-Net及U-Net++架构进行对比。实验结果表明，AKU-Net在自动化程度与检测精度上均优于现有方法，为临床环境中实现更高效可靠的光化性角化病评估提供了新的技术路径。
肿瘤(癌症)患者之家