Cassidy Bill, McBride Christian, Kendrick Connah, Reeves Neil D, Pappachan Joseph M, Fernandez Cornelius J, Chacko Elias, Brüngel Raphael, Friedrich Christoph M, Alotaibi Metib, AlWabel Abdullah Abdulaziz, Alderwish Mohammad, Lai Kuan-Ying, Yap Moi Hoon
Department of Computing and Mathematics, Manchester Metropolitan University, Dalton Building, Chester Street, Manchester, M1 5GD, UK.
Department of Computing and Mathematics, Manchester Metropolitan University, Dalton Building, Chester Street, Manchester, M1 5GD, UK.
Comput Biol Med. 2025 Jun;192(Pt A):110172. doi: 10.1016/j.compbiomed.2025.110172. Epub 2025 May 2.
Chronic wounds and associated complications present ever growing burdens for clinics and hospitals world wide. Venous, arterial, diabetic, and pressure wounds are becoming increasingly common globally. These conditions can result in highly debilitating repercussions for those affected, with limb amputations and increased mortality risk resulting from infection becoming more common. New methods to assist clinicians in chronic wound care are therefore vital to maintain high quality care standards. This paper presents an improved HarDNet segmentation architecture which integrates a contrast-eliminating component in the initial layers of the network to enhance feature learning. We also utilise a multi-colour space tensor merging process and adjust the harmonic shape of the convolution blocks to facilitate these additional features. We train our proposed model using wound images from light skinned patients and test the model on two test sets (one set with ground truth, and one without) comprising only darker skinned cases. Subjective ratings are obtained from clinical wound experts with intraclass correlation coefficient used to determine inter-rater reliability. For the dark skin tone test set with ground truth, when comparing the baseline results (DSC=0.6389, IoU=0.5350) with the results for the proposed model (DSC=0.7610, IoU=0.6620) we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1270). Measures from the qualitative analysis also indicate improvements in terms of high expert ratings, with improvements of >3% demonstrated when comparing the baseline model with the proposed model. This paper presents the first study to focus on darker skin tones for chronic wound segmentation using models trained only on wound images exhibiting lighter skin. Diabetes is highly prevalent in countries where patients have darker skin tones, highlighting the need for a greater focus on such cases. Additionally, we conduct the largest qualitative study to date for chronic wound segmentation. All source code for this study is available at: https://github.com/mmu-dermatology-research/hardnet-cws.
慢性伤口及相关并发症给全球各地的诊所和医院带来了日益沉重的负担。静脉性、动脉性、糖尿病性和压疮在全球范围内正变得越来越普遍。这些病症会给患者带来极为严重的不良影响，肢体截肢以及因感染导致的死亡风险增加的情况愈发常见。因此，帮助临床医生进行慢性伤口护理的新方法对于维持高质量的护理标准至关重要。本文提出了一种改进的HarDNet分割架构，该架构在网络的初始层集成了一个消除对比度的组件，以增强特征学习。我们还利用了多颜色空间张量合并过程，并调整卷积块的谐波形状以促进这些额外的特征。我们使用来自皮肤较浅患者的伤口图像训练我们提出的模型，并在两个测试集（一个有真实标注，一个没有）上测试该模型，这两个测试集仅包含皮肤较深的病例。从临床伤口专家那里获得主观评分，并使用组内相关系数来确定评分者间的可靠性。对于有真实标注的深色皮肤测试集，将基线结果（DSC = 0.6389，IoU = 0.5350）与所提出模型的结果（DSC = 0.7610，IoU = 0.6620）进行比较时，我们在骰子相似系数（提高了0.1221）和交并比（提高了0.1270）方面都有了改进。定性分析的结果也表明在专家高评分方面有了改进，将基线模型与所提出模型进行比较时，改进幅度超过了3%。本文呈现了第一项专注于使用仅在表现为较浅肤色的伤口图像上训练的模型进行慢性伤口分割的针对较深肤色的研究。糖尿病在患者肤色较深的国家非常普遍，这凸显了对这类病例给予更多关注的必要性。此外，我们进行了迄今为止最大规模的慢性伤口分割定性研究。本研究的所有源代码可在以下网址获取：https://github.com/mmu - dermatology - research/hardnet - cws。
