SADSNet:基于空间注意力机制和深度监督的肝脏和肝脏肿瘤鲁棒三维同步分割网络

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Sijing Yang, Yongbo Liang, Shang Wu, Peng Sun, Zhencheng Chen
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引用次数: 0

摘要

亮点- 引入数据扩增策略,在训练和学习阶段扩充所需的不同形态数据,提高算法对复杂多样的肿瘤形态CT图像的特征学习能力。- 通过在 LITS、3DIRCADb 和 SLIVER 数据集上的验证,证实了该方法的有效性:背景:从医学影像中准确提取肝脏和肝脏肿瘤是病灶定位和诊断、手术规划和术后监测的重要步骤。然而,有限的放射治疗人员和大量的图像使得这项工作耗时费力:本研究设计了一种空间注意力深度监督网络(SADSNet),用于同时自动分割肝脏和肿瘤:首先,在编码器和解码器的每一层引入自行设计的空间注意力模块,以提取不同尺度和分辨率的图像特征,帮助模型更好地捕捉肝脏肿瘤和精细结构。设计的空间注意力模块是通过与肝脏和肿瘤相关的两个门信号以及改变卷积核的大小来实现的;其次,在解码器的三层后面添加了深度监督,以辅助骨干网络进行特征学习,并改进梯度传播,增强鲁棒性:该方法在 LITS、3DIRCADb 和 SLIVER 数据集上进行了测试。对于肝脏,该方法获得的骰子相似系数分别为 97.03%、96.11% 和 97.40%,表面骰子相似系数分别为 81.98%、82.53% 和 86.29%,95% hausdorff 距离分别为 8.96 毫米、8.26 毫米和 3.79 毫米,平均表面距离分别为 1.54 毫米、1.19 毫米和 0.81 毫米。此外,它还实现了精确的肿瘤分割,在 LITS 和 3DIRCADb 上的骰子得分率分别为 87.81% 和 87.50%,表面骰子得分率分别为 89.63% 和 84.26%,95% hausdorff 距离分别为 12.96 mm 和 16.55 mm,平均表面距离分别为 1.11 mm 和 3.04 mm:实验结果表明,所提出的方法是有效的,而且优于其他一些方法。因此,该方法可为临床实践中的肝脏和肝脏肿瘤分割提供技术支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SADSNet: A robust 3D synchronous segmentation network for liver and liver tumors based on spatial attention mechanism and deep supervision.

Highlights: • Introduce a data augmentation strategy to expand the required different morphological data during the training and learning phase, and improve the algorithm's feature learning ability for complex and diverse tumor morphology CT images.• Design attention mechanisms for encoding and decoding paths to extract fine pixel level features, improve feature extraction capabilities, and achieve efficient spatial channel feature fusion.• The deep supervision layer is used to correct and decode the final image data to provide high accuracy of results.• The effectiveness of this method has been affirmed through validation on the LITS, 3DIRCADb, and SLIVER datasets.

Background: Accurately extracting liver and liver tumors from medical images is an important step in lesion localization and diagnosis, surgical planning, and postoperative monitoring. However, the limited number of radiation therapists and a great number of images make this work time-consuming.

Objective: This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors.

Method: Firstly, self-designed spatial attention modules are introduced at each layer of the encoder and decoder to extract image features at different scales and resolutions, helping the model better capture liver tumors and fine structures. The designed spatial attention module is implemented through two gate signals related to liver and tumors, as well as changing the size of convolutional kernels; Secondly, deep supervision is added behind the three layers of the decoder to assist the backbone network in feature learning and improve gradient propagation, enhancing robustness.

Results: The method was testing on LITS, 3DIRCADb, and SLIVER datasets. For the liver, it obtained dice similarity coefficients of 97.03%, 96.11%, and 97.40%, surface dice of 81.98%, 82.53%, and 86.29%, 95% hausdorff distances of 8.96 mm, 8.26 mm, and 3.79 mm, and average surface distances of 1.54 mm, 1.19 mm, and 0.81 mm. Additionally, it also achieved precise tumor segmentation, which with dice scores of 87.81% and 87.50%, surface dice of 89.63% and 84.26%, 95% hausdorff distance of 12.96 mm and 16.55 mm, and average surface distances of 1.11 mm and 3.04 mm on LITS and 3DIRCADb, respectively.

Conclusion: The experimental results show that the proposed method is effective and superior to some other methods. Therefore, this method can provide technical support for liver and liver tumor segmentation in clinical practice.

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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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