基于扩展U-Net结构的颈动脉斑块深度学习分割。

IF 2.5 4区 医学 Q1 ACOUSTICS
Nirvedh H Meshram, Carol C Mitchell, Stephanie Wilbrand, Robert J Dempsey, Tomy Varghese
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引用次数: 30

摘要

在这项工作中提出了使用深度学习的超声纵向b模式图像中的颈动脉斑块分割。我们报告101例颈动脉斑块严重狭窄患者。将标准U-Net与在瓶颈处使用扩展卷积层的扩展U-Net结构进行了比较。实现了带有边界框的全自动和半自动方法。对边界框误差导致的斑块分割性能下降进行了量化。我们发现边界盒显著提高了网络的性能,自动分割的U-Net Dice系数为0.48,半自动分割的U-Net Dice系数为0.83。与经验丰富的超声医师对同一斑块进行手工分割相比,经扩张的U-Net自动分割的Dice系数为0.55,半自动分割的Dice系数为0.84,结果也相似。在两个维度的边界框中,5%的误差将U-Net和扩展U-Net的Dice系数分别降低到0.79和0.80。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture.

Carotid plaque segmentation in ultrasound longitudinal B-mode images using deep learning is presented in this work. We report on 101 severely stenotic carotid plaque patients. A standard U-Net is compared with a dilated U-Net architecture in which the dilated convolution layers were used in the bottleneck. Both a fully automatic and a semi-automatic approach with a bounding box was implemented. The performance degradation in plaque segmentation due to errors in the bounding box is quantified. We found that the bounding box significantly improved the performance of the networks with U-Net Dice coefficients of 0.48 for automatic and 0.83 for semi-automatic segmentation of plaque. Similar results were also obtained for the dilated U-Net with Dice coefficients of 0.55 for automatic and 0.84 for semi-automatic when compared to manual segmentations of the same plaque by an experienced sonographer. A 5% error in the bounding box in both dimensions reduced the Dice coefficient to 0.79 and 0.80 for U-Net and dilated U-Net respectively.

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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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