This paper presents FUSegNet, a new model for foot ulcer segmentation in diabetes patients, which uses the pre-trained EfficientNet-b7 as a backbone to address the issue of limited training samples. A modified spatial and channel squeeze-and-excitation (scSE) module called parallel scSE or P-scSE is proposed that combines additive and max-out scSE. A new arrangement is introduced for the module by fusing it in the middle of each decoder stage. As the top decoder stage carries a limited number of feature maps, max-out scSE is bypassed there to form a shorted P-scSE. A set of augmentations, comprising geometric, morphological, and intensity-based augmentations, is applied before feeding the data into the network. The proposed model is first evaluated on a publicly available chronic wound dataset where it achieves a data-based dice score of 92.70%, which is the highest score among the reported approaches. The model outperforms other scSE-based UNet models in terms of Pratt's figure of merits (PFOM) scores in most categories, which evaluates the accuracy of edge localization. The model is then tested in the MICCAI 2021 FUSeg challenge, where a variation of FUSegNet called x-FUSegNet is submitted. The x-FUSegNet model, which takes the average of outputs obtained by FUSegNet using 5-fold cross-validation, achieves a dice score of 89.23%, placing it at the top of the FUSeg Challenge leaderboard. The source code for the model is available on https://github.com/mrinal054/FUSegNet.

翻译：本文提出FUSegNet，一种针对糖尿病患者足部溃疡分割的新型模型，采用预训练的EfficientNet-b7作为骨干网络以应对训练样本有限的问题。论文提出一种改进型空间与通道压缩激励模块，称为并行scSE（P-scSE），该模块融合了加性scSE与最大输出scSE。通过将模块嵌入每个解码阶段的中间位置，引入了一种新的结构排布。鉴于顶部解码阶段携带的特征图数量有限，在该阶段中绕过最大输出scSE以形成短接P-scSE结构。在将数据输入网络之前，应用了包含几何变换、形态学增强和强度增强在内的一系列数据增强方法。首先在公开慢性伤口数据集上评估所提模型，其基于数据的Dice系数达92.70%，为现有报道方法中的最高得分。在多数类别中，该模型在普拉特优值评分上优于其他基于scSE的UNet模型，该指标可评估边缘定位精度。随后在MICCAI 2021 FUSeg挑战赛中对该模型进行测试，提交了名为x-FUSegNet的FUSegNet变体。x-FUSegNet模型通过五折交叉验证对FUSegNet输出取平均，获得89.23%的Dice系数，位列FUSeg挑战赛排行榜首位。该模型源代码已发布于https://github.com/mrinal054/FUSegNet。