CO2Wounds-V2 Extended Chronic Wounds Dataset From Leprosy Patients with Segmentation and Detection Labels

Chronic wounds pose an ongoing health concern globally, largely due to the prevalence of conditions such as diabetes and leprosy's disease. The standard method of monitoring these wounds involves visual inspection by healthcare professionals, a practice that could present challenges for patients in remote areas with inadequate transportation and healthcare infrastructure. This has led to the development of algorithms designed for the analysis and follow-up of wound images, which perform image-processing tasks such as classification, detection, and segmentation. However, the effectiveness of these algorithms heavily depends on the availability of comprehensive and varied wound image data, which is usually scarce. This paper introduces the CO2Wounds-V2 dataset, an extended collection of RGB wound images from leprosy patients with their corresponding semantic segmentation annotations, aiming to enhance the development and testing of image-processing algorithms in the medical field.

慢性伤口在全球范围内构成了持续性的健康问题，这主要归因于糖尿病和麻风病等疾病的普遍存在。对这些伤口进行监测的标准方法涉及医疗专业人员进行的视觉检查，这一做法对于交通不便、医疗基础设施不健全的偏远地区患者而言可能存在挑战。这促使研究人员开发了旨在分析和跟踪伤口图像的算法，这些算法执行图像处理任务，如分类、检测和分割。然而，这些算法的有效性高度依赖于全面且多样化的伤口图像数据集，而这些数据通常稀缺。本文介绍了CO2Wounds-V2数据集，这是一个扩展的麻风病患者RGB伤口图像集合，并附有相应的语义分割标注，旨在促进医疗领域图像处理算法的开发和测试。