Schools of Electrical and Computer Engineering and Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.
IEEE Trans Med Imaging. 2010 Dec;29(12):1945-58. doi: 10.1109/TMI.2010.2050896.
This work provides a model for tubular structures, and devises an algorithm to automatically extract tubular anatomical structures from medical imagery. Our model fits many anatomical structures in medical imagery, in particular, various fiber bundles in the brain (imaged through diffusion-weighted magnetic resonance (DW-MRI)) such as the cingulum bundle, and blood vessel trees in computed tomography angiograms (CTAs). Extraction of the cingulum bundle is of interest because of possible ties to schizophrenia, and extracting blood vessels is helpful in the diagnosis of cardiovascular diseases. The tubular model we propose has advantages over many existing approaches in literature: fewer degrees-of-freedom over a general deformable surface hence energies defined on such tubes are less sensitive to undesirable local minima, and the tube (in 3-D) can be naturally represented by a 4-D curve (a radius function and centerline), which leads to computationally less costly algorithms and has the advantage that the centerline of the tube is obtained without additional effort. Our model also generalizes to tubular trees, and the extraction algorithm that we design automatically detects and evolves branches of the tree. We demonstrate the performance of our algorithm on 20 datasets of DW-MRI data and 32 datasets of CTA, and quantify the results of our algorithm when expert segmentations are available.
这项工作为管状结构提供了一个模型，并设计了一种算法，可以自动从医学图像中提取管状解剖结构。我们的模型适合于医学图像中的许多解剖结构，特别是大脑中的各种纤维束（通过扩散加权磁共振成像（DW-MRI）成像），如扣带束，以及计算机断层血管造影（CTA）中的血管树。提取扣带束是因为它可能与精神分裂症有关，而提取血管有助于心血管疾病的诊断。我们提出的管状模型优于文献中的许多现有方法：相对于一般的可变形表面，自由度更少，因此定义在这些管上的能量对不理想的局部最小值的敏感度较低，并且管（在 3D 中）可以通过 4D 曲线（半径函数和中心线）自然表示，这导致计算成本更低的算法，并且具有无需额外努力即可获得管中心线的优点。我们的模型也推广到管状树，并且我们设计的提取算法可以自动检测和演化树的分支。我们在 20 个 DW-MRI 数据集和 32 个 CTA 数据集上展示了我们算法的性能，并在有专家分割时量化了我们算法的结果。
