Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Graduate Program in Biomedical Engineering, The University of Western Ontario, London, ON, Canada.
Robarts Research Institute, The University of Western Ontario, London, ON, Canada; Department of Medical Biophysics, The University of Western Ontario, London, ON, Canada.
Med Image Anal. 2015 Jul;23(1):43-55. doi: 10.1016/j.media.2015.04.001. Epub 2015 Apr 17.
Pulmonary imaging using hyperpolarized (3)He/(129)Xe gas is emerging as a new way to understand the regional nature of pulmonary ventilation abnormalities in obstructive lung diseases. However, the quantitative information derived is completely dependent on robust methods to segment both functional and structural/anatomical data. Here, we propose an approach to jointly segment the lung cavity from (1)H and (3)He pulmonary magnetic resonance images (MRI) by constraining the spatial consistency of the two segmentation regions, which simultaneously employs the image features from both modalities. We formulated the proposed co-segmentation problem as a coupled continuous min-cut model and showed that this combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In particular, we introduced a dual coupled continuous max-flow model to study the convex relaxed coupled continuous min-cut model under a primal and dual perspective. This gave rise to an efficient duality-based convex optimization algorithm. We implemented the proposed algorithm in parallel using general-purpose programming on graphics processing unit (GPGPU), which substantially increased its computational efficiency. Our experiments explored a clinical dataset of 25 subjects with chronic obstructive pulmonary disease (COPD) across a wide range of disease severity. The results showed that the proposed co-segmentation approach yielded superior performance compared to single-channel image segmentation in terms of precision, accuracy and robustness.
利用超极化 (3)He/(129)Xe 气体进行肺部成像，正成为一种了解阻塞性肺部疾病中肺部通气异常的区域性特征的新方法。然而，所获得的定量信息完全依赖于稳健的方法来分割功能和结构/解剖数据。在这里，我们提出了一种从 (1)H 和 (3)He 肺部磁共振成像 (MRI) 中联合分割肺腔的方法，通过约束两个分割区域的空间一致性，同时利用两种模式的图像特征。我们将所提出的共分割问题表述为一个耦合连续最小割模型，并表明可以通过凸松弛全局和精确地解决这个组合优化问题。特别是，我们引入了一个对偶耦合连续最大流模型，从原语和对偶角度研究凸松弛的耦合连续最小割模型。这产生了一种有效的基于对偶的凸优化算法。我们使用图形处理单元 (GPGPU) 上的通用编程并行实现了所提出的算法，大大提高了计算效率。我们的实验探索了一个包含 25 名慢性阻塞性肺疾病 (COPD) 患者的临床数据集，涵盖了广泛的疾病严重程度。结果表明，与单通道图像分割相比，所提出的共分割方法在精度、准确性和鲁棒性方面具有更好的性能。
