School of Life and Environmental Science, Guilin University of Electronic Technology, Guilin, China.
School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, China.
J Xray Sci Technol. 2024;32(3):707-723. doi: 10.3233/XST-230312.
• 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.
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.
This study designs a spatial attention deep supervised network (SADSNet) for simultaneous automatic segmentation of liver and tumors.
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.
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.
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.
在训练和学习阶段引入数据增强策略来扩展所需的不同形态学数据，提高算法对复杂多样的肿瘤形态 CT 图像的特征学习能力。
设计编码和解码路径的注意力机制，提取精细像素级特征，提高特征提取能力，并实现高效的空间通道特征融合。
使用深度监督层来纠正和解码最终的图像数据，以提供高精度的结果。
该方法已在 LITS、3DIRCADb 和 SLIVER 数据集上得到验证，证明了其有效性。
准确地从医学图像中提取肝脏和肝肿瘤是病变定位和诊断、手术计划和术后监测的重要步骤。然而，由于放射治疗师人数有限，而图像数量庞大，这使得这项工作非常耗时。
本研究设计了一种用于肝脏和肿瘤自动分割的空间注意深度监督网络（SADSNet）。
首先，在编码器和解码器的每一层引入自设计的空间注意模块，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。所设计的空间注意模块通过与肝脏和肿瘤相关的两个门控信号以及改变卷积核的大小来实现；其次，在解码器的三层后面添加深度监督，以协助骨干网络进行特征学习并提高梯度传播，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，其获得的 Dice 相似系数分别为 97.03%、96.11%和 97.40%，表面 Dice 分别为 81.98%、82.53%和 86.29%，95% Hausdorff 距离分别为 8.96 mm、8.26 mm 和 3.79 mm，平均表面距离分别为 1.54 mm、1.19 mm 和 0.81 mm。此外，它还实现了精确的肿瘤分割，在 LITS 和 3DIRCADb 上的 Dice 评分分别为 87.81%和 87.50%，表面 Dice 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，该方法是有效的，优于一些其他方法。因此，该方法可为临床实践中的肝脏和肝肿瘤分割提供技术支持。
