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引用次数: 0
摘要
光学相干断层扫描(OCT)是一种非侵入性皮肤病评估的重要工具,可为诊断皮肤病提供有关表皮完整性的有用数据。尽管光学相干断层扫描具有诸多优点,但由于皮肤形态的多样性,它在准确、快速地分割表皮方面所面临的挑战限制了它的实用性。为解决这一问题,我们引入了轻量级分割网络(LS-Net),这是一种新型深度学习模型,结合了卷积神经网络的稳健局部特征提取能力和视觉转换器的长期信息处理能力。LS-Net 有一个深度卷积变换器,用于增强空间上下文关联,还有一个挤压-激发块,用于特征重新校准,确保精确分割的同时保持计算效率。我们的网络优于现有的方法,显示出较高的分割精度(平均 Dice:0.9624,平均 IoU:0.9468),同时显著降低了计算需求(浮点运算:1.131 G)。我们在获得的数据集上进一步验证了 LS-Net,显示了它在现实临床条件下对不同皮肤部位(如面部、手掌)的有效性。该模型有望提高 OCT 的诊断能力,使其成为皮肤科实践中的重要工具。
LS-Net: lightweight segmentation network for dermatological epidermal segmentation in optical coherence tomography imaging.
Optical coherence tomography (OCT) can be an important tool for non-invasive dermatological evaluation, providing useful data on epidermal integrity for diagnosing skin diseases. Despite its benefits, OCT's utility is limited by the challenges of accurate, fast epidermal segmentation due to the skin morphological diversity. To address this, we introduce a lightweight segmentation network (LS-Net), a novel deep learning model that combines the robust local feature extraction abilities of Convolution Neural Network and the long-term information processing capabilities of Vision Transformer. LS-Net has a depth-wise convolutional transformer for enhanced spatial contextualization and a squeeze-and-excitation block for feature recalibration, ensuring precise segmentation while maintaining computational efficiency. Our network outperforms existing methods, demonstrating high segmentation accuracy (mean Dice: 0.9624 and mean IoU: 0.9468) with significantly reduced computational demands (floating point operations: 1.131 G). We further validate LS-Net on our acquired dataset, showing its effectiveness in various skin sites (e.g., face, palm) under realistic clinical conditions. This model promises to enhance the diagnostic capabilities of OCT, making it a valuable tool for dermatological practice.
期刊介绍:
The journal''s scope encompasses fundamental research, technology development, biomedical studies and clinical applications. BOEx focuses on the leading edge topics in the field, including:
Tissue optics and spectroscopy
Novel microscopies
Optical coherence tomography
Diffuse and fluorescence tomography
Photoacoustic and multimodal imaging
Molecular imaging and therapies
Nanophotonic biosensing
Optical biophysics/photobiology
Microfluidic optical devices
Vision research.