Big Data Analytics and Visualization Laboratory, Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Department of Electrical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Sci Rep. 2020 Dec 14;10(1):21897. doi: 10.1038/s41598-020-78799-w.
Acute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https://github.com/uwm-bigdata/wound-segmentation .
急性和慢性伤口的病因不同，给世界各地的医疗保健系统带来了经济负担。预计到 2024 年，先进的伤口护理市场将超过 220 亿美元。伤口护理专业人员严重依赖图像和图像文档进行正确的诊断和治疗。不幸的是，缺乏专业知识可能导致对伤口病因的不正确诊断以及不准确的伤口管理和记录。在自然图像中自动分割伤口区域是诊断和护理方案的重要组成部分，因为测量伤口面积并在治疗中提供定量参数至关重要。各种深度学习模型在图像分析中取得了成功，包括语义分割。本文提出了一种基于 MobileNetV2 和连通分量标记的新型卷积框架，用于从自然图像中分割伤口区域。该模型的优点是其轻量级和计算强度低的架构。性能不会受到影响，并且可以与更深的神经网络相媲美。我们构建了一个包含 889 名患者的 1109 张足部溃疡图像的标注伤口图像数据集，以训练和测试深度学习模型。我们通过对各种分割神经网络进行全面的实验和分析，展示了我们方法的有效性和移动性。完整的实现可在 https://github.com/uwm-bigdata/wound-segmentation 获得。
