SIAM Journal on Imaging Sciences, Volume 13, Issue 4, Page 2189-2212, January 2020.
This work focuses on a variational approach to image segmentation based on the Ambrosio--Tortorelli functional. We propose an efficient algorithm, which combines the functional minimization with a smart choice of the computational mesh. With this aim, we resort to an anisotropic mesh adaptation procedure driven by an a posteriori recovery-based error analysis. We apply the proposed algorithm to a computed tomography dataset of a fractured pelvis to create a virtual model of the anatomy. The result is verified against a semiautomatic segmentation carried out using the ITK-SNAP tool. Furthermore, a physical replica of the virtual model is produced by means of fused filament fabrication technology to assess the appropriateness of the proposed solution in terms of resolution-quality balance for three-dimensional printing production.

中文翻译：

---

各向异性自适应网格用于图像分割：在三维医学数据中的应用

SIAM影像科学杂志，第13卷，第4期，第2189-2212页，2020年1月。
这项工作着重于基于Ambrosio-Tortorelli功能的变体图像分割方法。我们提出了一种有效的算法，该算法将功能最小化与对计算网格的明智选择相结合。为此，我们求助于各向异性网格自适应过程，该过程由基于后验恢复的误差分析驱动。我们将提出的算法应用于骨盆骨折的计算机断层扫描数据集，以创建解剖结构的虚拟模型。使用ITK-SNAP工具对结果进行半自动验证。此外，通过熔融长丝制造技术生产虚拟模型的物理副本，以根据三维打印生产的分辨率-质量平衡来评估所提出解决方案的适当性。