Aims

Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel radiation dermatitis segmentation system based on convolutional neural networks (CNNs) to consistently evaluate radiation dermatitis.

Materials and methods

The radiation dermatitis segmentation system is designed to segment the radiation dermatitis occurrence area using skin photographs and skin-dose distribution. A CNN architecture with a dilated convolution layer and skip connection was designed to estimate the radiation dermatitis area. Seventy-three skin photographs obtained from patients undergoing radiotherapy were collected for training and testing. The ground truth of radiation dermatitis segmentation is manually delineated from the skin photograph by an experienced radiation oncologist and medical physicist. We converted the skin photographs to RGB (red-green-blue) and CIELAB (lightness (L^∗), red-green (a^∗) and blue-yellow (b^∗)) colour information and trained the network to segment faint and severe radiation dermatitis using three different input combinations: RGB, RGB + CIELAB (RGBLAB) and RGB + CIELAB + skin-dose distribution (RGBLAB_D). The proposed system was evaluated using the Dice similarity coefficient (DSC), sensitivity, specificity and normalised Matthews correlation coefficient (nMCC). A paired t-test was used to compare the results of different segmentation performances.

Results

Optimal data composition was observed in the network trained for radiation dermatitis segmentation using skin photographs and skin-dose distribution. The average DSC, sensitivity, specificity and nMCC values of RGBLAB_D were 0.62, 0.61, 0.91 and 0.77, respectively, in faint radiation dermatitis, and 0.69, 0.78, 0.96 and 0.83, respectively, in severe radiation dermatitis.

Conclusion

Our study showed that CNN-based radiation dermatitis segmentation in skin photographs of patients undergoing radiotherapy can describe radiation dermatitis severity and pattern. Our study could aid in objectifying the radiation dermatitis grading and analysing the reliable correlation between dosimetric factors and the morphology of radiation dermatitis.

中文翻译：

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在皮肤照片中使用深度学习分割对放射性皮炎进行量化和分级的新方法

宗旨

放射性皮炎的客观评价对于临床实践中分析放射性皮炎的严重程度与剂量分布之间的相关性以及临床试验中的可靠报告具有重要意义。我们开发了一种基于卷积神经网络 (CNN) 的新型放射性皮炎分割系统，以持续评估放射性皮炎。

材料和方法

放射性皮炎分割系统旨在利用皮肤照片和皮肤剂量分布来分割放射性皮炎发生区域。设计了具有扩张卷积层和跳跃连接的 CNN 架构来估计放射性皮炎区域。收集了接受放疗的患者的 73 张皮肤照片用于训练和测试。放射性皮炎分割的基本事实是由经验丰富的放射肿瘤学家和医学物理学家从皮肤照片中手动描绘出来的。我们将皮肤照片转换为 RGB（红-绿-蓝）和 CIELAB（亮度（L ^*）、红-绿（a ^*）和蓝-黄（b ^*）)) 颜色信息，并训练网络使用三种不同的输入组合来分割微弱和严重的放射性皮炎：RGB、RGB + CIELAB (RGBLAB) 和 RGB + CIELAB + 皮肤剂量分布 (RGBLAB_D)。使用 Dice 相似系数 (DSC)、灵敏度、特异性和归一化马修斯相关系数 (nMCC) 对所提出的系统进行了评估。配对t检验用于比较不同分割性能的结果。

结果

在使用皮肤照片和皮肤剂量分布进行放射性皮炎分割训练的网络中观察到最佳数据组成。RGBLAB_D 的平均 DSC、灵敏度、特异性和 nMCC 值在微弱放射性皮炎中分别为 0.62、0.61、0.91 和 0.77，在严重放射性皮炎中分别为 0.69、0.78、0.96 和 0.83。

结论

我们的研究表明，接受放射治疗的患者皮肤照片中基于 CNN 的放射性皮炎分割可以描述放射性皮炎的严重程度和模式。我们的研究有助于客观化放射性皮炎分级，并分析剂量学因素与放射性皮炎形态之间的可靠相关性。