Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.
Department of Computing, The Hong Kong Polytechnic University, Hong Kong Special Administrative Region.
Ultrasonics. 2024 Aug;142:107358. doi: 10.1016/j.ultras.2024.107358. Epub 2024 Jun 10.
Stiffness measurement using shear wave propagation velocity has been the most common non-invasive method for liver fibrosis assessment. The velocity is captured through a trace recorded by transient ultrasonographic elastography, with the slope indicating the velocity of the wave. However, due to various factors such as noise and shear wave attenuation, detecting shear wave trajectory on wave propagation maps is a challenging task. In this work, we made the first attempt to use deep learning methods for shear wave trajectory detection on wave propagation maps. Specifically, we adopted five deep learning models in this task and evaluated them by using a well-acknowledged metric based on EA-Angular-Score (EAA) and task-specific metric based on Young s-Score (Ys) in the line-detection field. Furthermore, we proposed an end-to-end framework based on a Transformer and Hough transform, named Transformer-enhanced Hough Transform (TEHT). It took a wave propagation map as input image and directly output the slope of the shear wave trajectory. The framework extracts multi-scale local features from wave propagation maps, employs a deformable attention mechanism for feature fusion, identifies the target line using the Hough transform's voting mechanism, and calculates the contribution of each scale through channel attention. Wave propagation maps from 68 patients were utilized in this study, with manual annotation performed by a rater who was trained as a radiologist, serving as the reference value. The evaluation revealed that the SLNet model exhibited F-measure of EA and Ys values as 40.33 % and 40.72 %, respectively, while the TEHT model showed F-measure of EA and Ys values as 80.96 % and 98.00 %, respectively. TEHT yielded significantly better performance than other deep learning models. Moreover, TEHT demonstrated strong concordance with the gold standard, yielding R values of 0.967 and 0.968 for velocity and liver stiffness, respectively. The present study therefore suggests the application of the TEHT model for assessing liver fibrosis owing to its superiority among the five deep learning models.
使用剪切波传播速度进行硬度测量一直是评估肝纤维化的最常用的非侵入性方法。该速度是通过瞬态超声弹性成像记录的轨迹来捕获的，斜率表示波的速度。然而，由于噪声和剪切波衰减等各种因素的影响，在波传播图上检测剪切波轨迹是一项具有挑战性的任务。在这项工作中，我们首次尝试使用深度学习方法在波传播图上检测剪切波轨迹。具体来说，我们在这项任务中采用了五种深度学习模型，并使用基于 EA-Angular-Score (EAA) 的公认指标和基于线检测领域的 Young's-Score (Ys) 的特定任务指标对其进行了评估。此外，我们提出了一种基于 Transformer 和 Hough 变换的端到端框架，称为 Transformer-enhanced Hough Transform (TEHT)。它以波传播图作为输入图像，直接输出剪切波轨迹的斜率。该框架从波传播图中提取多尺度局部特征，采用可变形注意力机制进行特征融合，使用 Hough 变换的投票机制识别目标线，并通过通道注意力计算每个尺度的贡献。本研究共使用了 68 名患者的波传播图，由经过放射科医生培训的评分员进行手动标注，作为参考值。评估结果表明，SLNet 模型的 EA 和 Ys 值的 F-measure 分别为 40.33%和 40.72%，而 TEHT 模型的 EA 和 Ys 值的 F-measure 分别为 80.96%和 98.00%。TEHT 的性能明显优于其他深度学习模型。此外，TEHT 与金标准具有很强的一致性，速度和肝硬度的 R 值分别为 0.967 和 0.968。因此，由于其在五种深度学习模型中的优越性，本研究建议使用 TEHT 模型评估肝纤维化。
