Lim Kyoung Yoon, Ko Jae Eun, Hwang Yoo Na, Lee Sang Goo, Kim Sung Min
Department of Medical Device and Healthcare, Dongguk University, Seoul 04620, Republic of Korea.
Department of Regulatory Science for Medical Device, Dongguk University, Seoul 04620, Republic of Korea.
Diagnostics (Basel). 2025 Jan 7;15(2):118. doi: 10.3390/diagnostics15020118.
Liver cancer has a high mortality rate worldwide, and clinicians segment liver vessels in CT images before surgical procedures. However, liver vessels have a complex structure, and the segmentation process is conducted manually, so it is time-consuming and labor-intensive. Consequently, it would be extremely useful to develop a deep learning-based automatic liver vessel segmentation method. As a segmentation method, UNet is widely used as a baseline, and a multi-scale block or attention module has been introduced to extract context information. In recent machine learning efforts, not only has the global context extraction been improved by introducing Transformer, but a method to reinforce the edge area has been proposed. However, the data preprocessing step still commonly uses general augmentation methods, such as flip, rotation, and mirroring, so it does not perform robustly on images of varying brightness or contrast levels. We propose a method of applying image augmentation with different HU windowing values. In addition, to minimize the false negative area, we propose TransRAUNet, which introduces a reverse attention module (RAM) that can focus edge information to the baseline TransUNet. The proposed architecture solves context loss for small vessels by applying edge module (RAM) in the upsampling phase. It can also generate semantic feature maps that allows it to learn edge, global context, and detail location by combining high-level edge and low-level context features. In the 3Dricadb dataset, the proposed model achieved a DSC of 0.948 and a sensitivity of 0.944 in liver vessel segmentation. This study demonstrated that the proposed augmentation method is effective and robust by comparisons with the model without augmentation and with the general augmentation method. Additionally, an ablation study showed that RAM has improved segmentation performance compared to TransUNet. Compared to prevailing state-of-the-art methods, the proposed model showed the best performance for liver vessel segmentation. TransRAUnet is expected to serve as a navigation aid for liver resection surgery through accurate liver vessel and tumor segmentation.
肝癌在全球范围内具有较高的死亡率，临床医生在手术前会在CT图像中对肝血管进行分割。然而，肝血管结构复杂，分割过程是手动进行的，因此既耗时又费力。因此，开发一种基于深度学习的自动肝血管分割方法将非常有用。作为一种分割方法，U-Net被广泛用作基线，并引入了多尺度块或注意力模块来提取上下文信息。在最近的机器学习研究中，不仅通过引入Transformer改进了全局上下文提取，还提出了一种强化边缘区域的方法。然而，数据预处理步骤仍然普遍使用一般的增强方法，如翻转、旋转和镜像，因此在亮度或对比度不同的图像上表现不够稳健。我们提出了一种应用不同HU窗宽值进行图像增强的方法。此外，为了最小化假阴性区域，我们提出了TransRAUNet，它引入了一个反向注意力模块（RAM），可以将边缘信息聚焦到基线TransUNet上。所提出的架构通过在向上采样阶段应用边缘模块（RAM）解决了小血管的上下文丢失问题。它还可以生成语义特征图，通过结合高级边缘和低级上下文特征来学习边缘、全局上下文和细节位置。在3Dricadb数据集中，所提出的模型在肝血管分割中实现了0.948的DSC和0.944的灵敏度。通过与未增强模型和一般增强方法进行比较，本研究表明所提出的增强方法是有效且稳健的。此外，消融研究表明，与TransUNet相比，RAM提高了分割性能。与现有的最先进方法相比，所提出的模型在肝血管分割方面表现出最佳性能。TransRAUnet有望通过准确的肝血管和肿瘤分割为肝切除手术提供导航辅助。
