Automated Segmentation of Wound Bed Tissue from Clinical Photographs: A Benchmark of Five Deep Learning Architectures Under a Shared Protocol

Accurate characterisation of the wound bed, the relative proportions of granulation, slough and necrotic tissue described by the red-yellow-black model, is central to monitoring healing and guiding debridement in chronic-wound care, yet in routine practice, it is judged visually and is poorly reproducible between clinicians. We investigated automatic, pixel-level segmentation of these three classes within the wound region of interest. A corpus of 404 photographs was curated from a public wound image collection and densely annotated by wound care clinicians with a purpose-built, cluster-assisted labelling tool. Five deep learning architectures spanning convolutional, transformer, hybrid and ensemble families were trained on one frozen data split under a largely shared pipeline, with limited, fully documented architecture-specific tuning, and scored on a shared 62-image test set. The models fell within a narrow mean intersection-over-union band (0.748-0.818). The strongest single model, a purely convolutional network, was statistically indistinguishable from the best ensemble, and simply averaging already-trained convolutional and transformer models matched that ensemble at no additional training cost. Slough remained consistently the hardest tissue. Translated into clinical units, per-wound tissue percentages were recovered with small mean bias (within ± 5 percentage points) but wide single-image limits of agreement (about ± 25-50 percentage points), so the measurement suits aggregate and longitudinal assessment rather than single-image reading. Within this dataset, split and training protocol, the choice among the strong hierarchical architectures was not decisive: a single strong model is the pragmatic option for clinical deployment, with an ensemble reserved for maximal accuracy and class balance.