Foot ulcer is a common complication of diabetes mellitus and, associated with substantial morbidity and mortality, remains a major risk factor for lower leg amputations. Extracting accurate morphological features from foot wounds is crucial for appropriate treatment. Although visual inspection by a medical professional is the common approach for diagnosis, this is subjective and error-prone, and computer-aided approaches thus provide an interesting alternative. Deep learning-based methods, and in particular convolutional neural networks (CNNs), have shown excellent performance for various tasks in medical image analysis including medical image segmentation. In this paper, we propose an ensemble approach based on two encoder-decoder-based CNN models, namely LinkNet and U-Net, to perform foot ulcer segmentation. To deal with a limited number of available training samples, we use pre-trained weights (EfficientNetB1 for the LinkNet model and EfficientNetB2 for the U-Net model) and perform further pre-training using the Medetec dataset while also applying a number of morphological-based and colour-based augmentation techniques. To boost the segmentation performance, we incorporate five-fold cross-validation, test time augmentation and result fusion. Applied on the publicly available chronic wound dataset and the MICCAI 2021 Foot Ulcer Segmentation (FUSeg) Challenge, our method achieves state-of-the-art performance with data-based Dice scores of 92.07% and 88.80%, respectively, and is the top ranked method in the FUSeg challenge leaderboard. The Dockerised guidelines, inference codes and saved trained models are publicly available at https://github.com/masih4/Foot_Ulcer_Segmentation.

翻译：足部溃疡是糖尿病常见的并发症,与大量发病率和死亡率相关,仍然是低腿截肢的一个主要风险因素。从脚部伤口中提取准确的形态特征对于适当治疗至关重要。尽管由医学专业人员进行视觉检查是诊断的常见方法,但这是主观和容易出错的,因此计算机辅助方法提供了有趣的替代方法。深层次学习方法,特别是神经神经网络(CNNN),在医学图像分析,包括医疗图像分解方面表现出了出色的表现。在本文中,我们建议基于两种基于编码、以颜色为基础的CNN模型(即LinkNet和U-Net)的混合方法,以进行足部分解。要处理数量有限的现有培训样本,我们使用预先训练的重量(LinkNet模型的Efficent Net1和UnativeNetB2, U-Net模型的Offical ),并使用基于Mdeterecal-Ferlick 数据集,同时运用基于变形和基于颜色的变形模型的Servical-egardSeral-Equal-deal dalalation Supal Procialation 数据系统,我们用了五种基于Scial-deal-deal-deal-dealation 数据,在Suplationalational-dealational dalationalationalation 20 和可调制成的Supal-deal-deal-deal-deal-deal-dealmentalmentaldaldaldaldal 数据。