Raj Ritesh, Londhe Narendra D, Sonawane Rajendra
Electrical Engineering Department, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492010, India.
Electrical Engineering Department, National Institute of Technology Raipur, Raipur, Chhattisgarh, 492010, India.
Comput Methods Programs Biomed. 2021 Jul;206:106123. doi: 10.1016/j.cmpb.2021.106123. Epub 2021 Apr 23.
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.
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.
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.
由于成像环境不受约束且背景不均匀，从数字图像中自动分割银屑病皮损是一项具有挑战性的任务。现有的用于银屑病皮损自动分割的传统或基于机器学习的图像处理方法存在若干局限性，例如依赖手工特征、人工干预、随着数据量增加性能降低且不可靠、用于去除背景或其他伪影的人工预处理步骤等。方法：在本文中，我们提出一种基于深度学习模型的全自动方法，该模型使用迁移学习范式从银屑病患者不同身体部位的数字图像中分割银屑病皮损。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主干。所提出的模型使用自行准备的银屑病数据集以及相应的皮损分割标注进行重新训练。
使用五折交叉验证技术评估所提出方法的性能。对于预期任务，所提出的方法实现了平均骰子相似性指数为0.948和杰卡德指数为0.901。与从零开始训练所提出的模型相比，迁移学习分别使骰子相似性指数和杰卡德指数度量的分割性能提高了约4.4%和7.6%。
与现有最先进的分割模型和现有文献进行的广泛对比分析验证了所提出框架的良好性能。因此，我们提出的方法将为银屑病皮损的客观面积评估提供依据。
