Segmenting skin ulcers and measuring the wound area using deep convolutional networks

Background and objectives: Bedridden patients presenting chronic skin ulcers often need to be examined at home. Healthcare professionals follow the evolution of the patients' condition by regularly taking pictures of the wounds, as different aspects of the wound can indicate the healing stages of the ulcer, including depth, location, and size. The manual measurement of the wounds' size is often inaccurate, time-consuming, and can also cause discomfort to the patient. In this work, we propose the Automatic Skin Ulcer Region Assessment ASURA framework to accurately segment the wound and automatically measure its size. Methods: ASURA uses an encoder/decoder deep neural network to perform the segmentation, which detects the measurement ruler/tape present in the image and estimates its pixel density. Results: Experimental results show that ASURA outperforms the state-of-the-art methods by up to 16% regarding the Dice score, being able to correctly segment the wound with a Dice score higher than 90%. ASURA automatically estimates the pixel density of the images with a relative error of 5%. When using a semi-automatic approach, ASURA was able to estimate the area of the wound in square centimeters with a relative error of 14%. Conclusions: The results show that ASURA is well-suited for the problem of segmenting and automatically measuring skin ulcers. (C) 2020 Elsevier B.V. All rights reserved.