FUSegNet: A Deep Convolutional Neural Network for Foot Ulcer Segmentation

TL;DR

FUSegNet is a novel deep learning model utilizing EfficientNet-b7 and a modified scSE module for accurate foot ulcer segmentation, achieving top scores on public datasets and challenges.

Contribution

Introduces FUSegNet with a new parallel scSE module and data augmentation strategies, improving foot ulcer segmentation accuracy over existing models.

Findings

Achieved 92.70% dice score on a public wound dataset.

Outperformed other models in PFOM scores for edge localization.

Secured top position in the MICCAI 2021 FUSeg challenge.

Abstract

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.