Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada.
Robarts Research Institute, Western University, London, ON, Canada; Biomedical Engineering Graduate Program, Western University, London, ON, Canada.
Med Image Anal. 2015 Dec;26(1):120-32. doi: 10.1016/j.media.2015.08.004. Epub 2015 Sep 2.
Three-dimensional (3D) measurements of peripheral arterial disease (PAD) plaque burden extracted from fast black-blood magnetic resonance (MR) images have shown to be more predictive of clinical outcomes than PAD stenosis measurements. To this end, accurate segmentation of the femoral artery lumen and outer wall is required for generating volumetric measurements of PAD plaque burden. Here, we propose a semi-automated algorithm to jointly segment the femoral artery lumen and outer wall surfaces from 3D black-blood MR images, which are reoriented and reconstructed along the medial axis of the femoral artery to obtain improved spatial coherence between slices of the long, thin femoral artery and to reduce computation time. The developed segmentation algorithm enforces two priors in a global optimization manner: the spatial consistency between the adjacent 2D slices and the anatomical region order between the femoral artery lumen and outer wall surfaces. The formulated combinatorial optimization problem for segmentation is solved globally and exactly by means of convex relaxation using a coupled continuous max-flow (CCMF) model, which is a dual formulation to the convex relaxed optimization problem. In addition, the CCMF model directly derives an efficient duality-based algorithm based on the modern multiplier augmented optimization scheme, which has been implemented on a GPU for fast computation. The computed segmentations from the developed algorithm were compared to manual delineations from experts using 20 black-blood MR images. The developed algorithm yielded both high accuracy (Dice similarity coefficients ≥ 87% for both the lumen and outer wall surfaces) and high reproducibility (intra-class correlation coefficient of 0.95 for generating vessel wall area), while outperforming the state-of-the-art method in terms of computational time by a factor of ≈ 20.
从快速黑血磁共振（MR）图像中提取的外周动脉疾病（PAD）斑块负担的三维（3D）测量结果表明，其比 PAD 狭窄测量结果更能预测临床结果。为此，需要对股动脉管腔和外壁进行准确分割，以便生成 PAD 斑块负担的容积测量值。在这里，我们提出了一种半自动算法，用于从 3D 黑血 MR 图像中共同分割股动脉管腔和外壁表面，这些图像沿股动脉的内侧轴重新定向和重建，以提高长而薄的股动脉的切片之间的空间一致性，并减少计算时间。所开发的分割算法以全局优化的方式强制执行两个先验：相邻 2D 切片之间的空间一致性和股动脉管腔与外壁之间的解剖区域顺序。通过使用耦合连续最大流（CCMF）模型以凸松弛的方式全局且精确地解决了用于分割的组合优化问题，CCMF 模型是凸松弛优化问题的对偶形式。此外，CCMF 模型直接基于现代乘子增强优化方案推导出一种有效的对偶算法，该算法已在 GPU 上实现，以便快速计算。使用 20 个黑血 MR 图像将开发的算法计算出的分割结果与专家的手动描绘进行了比较。该算法的计算分割结果既具有高精度（管腔和外壁表面的 Dice 相似系数均≥87％），又具有高度可重复性（生成血管壁面积的组内相关系数为 0.95），同时在计算时间方面优于最先进的方法，快了约 20 倍。
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