Automated psoriasis lesion segmentation from unconstrained environment using residual U-Net with transfer learning. (July 2021) - Raj, Ritesh; Londhe, Narendra D.; Sonawane, Rajendra
Highlights: Proposed a fully automatic approach for psoriasis lesion segmentation from the digital images acquired in the unconstrained environment having non-uniform or complex background. Implementation of deep residual learning-based U-Net model using the transfer learning paradigm for the automatic psoriasis lesion segmentation from the digital images of different body regions of the psoriasis patients. The segmentation performance of the proposed methodology is evaluated by using five different performance metrics (i.e. Dice Similarity Index, Jaccard Index, Accuracy, Sensitivity, and Specificity) and also validated with a five-fold cross-validation technique. The effect of transfer learning, image size, and batch size in the performance of the proposed model for the intended task is analyzed and compared. The proposed methodology is extensively compared with the other deep learning segmentation models and existing literature to validate the promising performance of the proposed methodology. Abstract: 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.