Background and objective

The automatic segmentation of psoriasis lesions from digital images is a challenging task due to the unconstrained imaging environment and non-uniform background. Existing conventional or machine learning-based image processing methods for automatic psoriasis lesion segmentation have several limitations, such as dependency on manual features, human intervention, less and unreliable performance with an increase in data, manual pre-processing steps for removal of background or other artifacts, etc.

Methods

In this paper, we propose a fully automatic approach based on a deep learning model using the transfer learning paradigm for the segmentation of psoriasis lesions from the digital images of different body regions of the psoriasis patients. The proposed model is based on U-Net architecture whose encoder path utilizes a pre-trained residual network model as a backbone. The proposed model is retrained with a self-prepared psoriasis dataset and corresponding segmentation annotation of the lesion.

Results

The performance of the proposed method is evaluated using a five-fold cross-validation technique. The proposed method achieves an average Dice Similarity Index of 0.948 and Jaccard Index of 0.901 for the intended task. The transfer learning provides an improvement in the segmentation performance of about 4.4% and 7.6% in Dice Similarity Index and Jaccard Index metric respectively, as compared to the training of the proposed model from scratch.

Conclusions

An extensive comparative analysis with the state-of-the-art segmentation models and existing literature validates the promising performance of the proposed framework. Hence, our proposed method will provide a basis for an objective area assessment of psoriasis lesions.

中文翻译：

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使用残留的U-Net和转移学习从不受限制的环境中自动进行牛皮癣病变分割

背景和目标

由于不受约束的成像环境和不均匀的背景，从数字图像中自动分割牛皮癣病变是一项艰巨的任务。现有的基于常规或基于机器学习的用于自动牛皮癣病变分割的图像处理方法具有多个局限性，例如对手动功能的依赖，人为干预，数据增加时性能降低和不可靠，用于去除背景的手动预处理步骤或其他文物等

方法

在本文中，我们提出了一种基于深度学习模型的全自动方法，该方法使用转移学习范式从牛皮癣患者不同身体部位的数字图像中分割牛皮癣病变。所提出的模型基于U-Net架构，其编码器路径利用预训练的残差网络模型作为骨干网。所提出的模型使用自我准备的牛皮癣数据集和相应的病变分割注释进行了重新训练。

结果

使用五重交叉验证技术评估了所提出方法的性能。拟议的方法实现了预期任务的平均骰子相似性指数为0.948，Jaccard指数为0.901。与从头开始训练提出的模型相比，转移学习分别在Dice相似性指数和Jaccard指数指标上提供了约4.4％和7.6％的细分性能改进。

结论

使用最新的细分模型和现有文献进行的广泛比较分析验证了所提出框架的良好前景。因此，我们提出的方法将为牛皮癣病变的客观区域评估提供基础。