利用多色彩空间张量融合的增强型谐波密集连接混合Transformer网络架构用于慢性伤口分割
An Enhanced Harmonic Densely Connected Hybrid Transformer Network Architecture for Chronic Wound Segmentation Utilising Multi-Colour Space Tensor Merging
AI总结：
本文提出改进的HarDNet架构，集成对比消除组件、多色彩空间张量融合及谐波卷积块调整，使用浅肤色数据训练并在深肤色测试集上验证，Dice和IoU显著提升，为首个专注深肤色慢性伤口分割的研究。
摘要：
慢性伤口及其相关并发症给全球诊所和医院带来越来越大的负担。静脉、动脉、糖尿病和压力性伤口在全球范围内正变得越来越普遍。这些疾病可能给受影响者带来严重削弱生活质量的后果，因感染导致的截肢和死亡率风险增加正变得更为常见。因此，辅助临床医生进行慢性伤口护理的新方法对于维持高质量护理标准至关重要。本文提出了一种改进的HarDNet分割架构，该架构在网络初始层集成了对比消除组件以增强特征学习。我们还利用多色彩空间张量融合过程，并调整卷积块的谐波形状以促进这些附加特征。我们使用浅肤色患者的伤口图像训练所提出的模型，并在仅包含深肤色病例的两个测试集（一个带有真实标注，一个不带）上测试该模型。从临床伤口专家处获得主观评分，并使用组内相关系数确定评分者间可靠性。对于带有真实标注的深肤色测试集，我们在Dice相似系数（+0.1221）和交并比（+0.1274）方面展示了改进。定性分析显示专家评分较高，在比较基线模型与所提出模型时展示了>3%的改进。本文提出了首个专注于深肤色慢性伤口分割的研究，使用仅在浅肤色伤口图像上训练的模型。糖尿病在患者肤色较深的国家高度流行，凸显了对这类病例更多关注的需求。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。
英文摘要：
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, we demonstrate improvements in terms of Dice similarity coefficient (+0.1221) and intersection over union (+0.1274). Qualitative analysis showed 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
