  Identifying individual tissues, so-called tissue segmentation, in diabetic
foot ulcer (DFU) images is a challenging task and little work has been
published, largely due to the limited availability of a clinical image dataset.
To address this gap, we have created a DFUTissue dataset for the research
community to evaluate wound tissue segmentation algorithms. The dataset
contains 110 images with tissues labeled by wound experts and 600 unlabeled
images. Additionally, we conducted a pilot study on segmenting wound
characteristics including fibrin, granulation, and callus using deep learning.
Due to the limited amount of annotated data, our framework consists of both
supervised learning (SL) and semi-supervised learning (SSL) phases. In the SL
phase, we propose a hybrid model featuring a Mix Transformer (MiT-b3) in the
encoder and a CNN in the decoder, enhanced by the integration of a parallel
spatial and channel squeeze-and-excitation (P-scSE) module known for its
efficacy in improving boundary accuracy. The SSL phase employs a
pseudo-labeling-based approach, iteratively identifying and incorporating
valuable unlabeled images to enhance overall segmentation performance.
Comparative evaluations with state-of-the-art methods are conducted for both SL
and SSL phases. The SL achieves a Dice Similarity Coefficient (DSC) of 84.89%,
which has been improved to 87.64% in the SSL phase. Furthermore, the results
are benchmarked against two widely used SSL approaches: Generative Adversarial
Networks and Cross-Consistency Training. Additionally, our hybrid model
outperforms the state-of-the-art methods with a 92.99% DSC in performing binary
segmentation of DFU wound areas when tested on the Chronic Wound dataset. Codes
and data are available at https://github.com/uwm-bigdata/DFUTissueSegNet.
