DCA-U-Net:用于小鼠皮肤OCT图像激光热损伤区域分割的深度学习网络。

IF 1.3 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Chenliang Xu, Qiong Ma, Jingyuan Wu, Yu Wei, Qi Liu, Qingyu Cai, Haiyang Sun, Xiaoan Tang, Hongxiang Kang
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引用次数: 0

摘要

激光热损伤是临床治疗中常见的一种皮肤损伤形式,准确评估损伤程度和治疗效果对患者康复至关重要。近年来,深度学习模型越来越多地应用于皮肤损伤区域的自动分割。然而,现有的方法往往存在参数过多的问题,在减少模型参数数量的情况下,分割精度会明显下降,从而限制了其临床适用性。为了解决这个问题,我们提出了一种高效且轻量级的分割模型,即基于U-Net的扩展卷积注意力U-Net (DCA-U-Net)。DCA-U-Net通过结合更高效的扩展卷积神经网络块(DCB)和双模块注意力块(DMAB),显著减少了参数数量,同时提高了特征提取能力和分割精度。与标准U-Net相比,我们的模型减少了33%的参数数量。在两组不同的小鼠皮肤激光热损伤光学相干层析成像(OCT)数据集上的实验结果表明,该模型在数据量不足或充足的情况下都具有较好的分割性能。这些改进不仅增强了模型准确识别皮肤热损伤区域的能力,而且在保持较高分割精度的同时大幅降低了计算成本,为皮肤激光热损伤的精确诊断和治疗提供了有希望的技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DCA-U-Net: a deep learning network for segmentation of laser-induced thermal damage regions in mouse skin OCT images.

Laser-induced thermal injury is a common form of skin damage in clinical treatment, and accurately assessing the extent of injury and treatment efficacy is crucial for patient recovery. In recent years, deep learning models have been increasingly applied to the automatic segmentation of skin injury regions. However, existing methods often suffer from a large number of parameters, leading to a significant decline in segmentation accuracy when reducing the number of model parameters, thus limiting their clinical applicability. To address this issue, we propose an efficient and lightweight segmentation model, Dilated ConvNeXT Attention U-Net (DCA-U-Net), based on U-Net. By incorporating the more efficient Dilated ConvNeXT Block (DCB) and Dual Module Attention Block (DMAB), DCA-U-Net significantly reduces the number of parameters while simultaneously improving feature extraction capability and segmentation accuracy. Compared to the standard U-Net, our model reduces the number of parameters by 33%. Experimental results on two different sections of mouse skin laser thermal damage Optical Coherence Tomography (OCT) datasets show that our model has better segmentation performance with insufficient or sufficient amount of data. These improvements not only enhance the model's ability to accurately identify skin thermal injury regions, but also substantially reduce computational costs while maintaining high segmentation accuracy, offering promising technical support for the precise diagnosis and treatment of skin laser thermal injuries.

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来源期刊
Biomedical Physics & Engineering Express
Biomedical Physics & Engineering Express RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
2.80
自引率
0.00%
发文量
153
期刊介绍: BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.
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