In the context of biomarker discovery and molecular characterization of diseases, laser capture microdissection is a highly effective approach to extract disease-specific regions from complex, heterogeneous tissue samples. For the extraction to be successful, these regions have to satisfy certain constraints in size and shape and thus have to be decomposed into feasible fragments. We model this problem of constrained shape decomposition as the computation of optimal feasible decompositions of simple polygons. We use a skeleton-based approach and present an algorithmic framework that allows the implementation of various feasibility criteria as well as optimization goals. Motivated by our application, we consider different constraints and examine the resulting fragmentations. We evaluate our algorithm on lung tissue samples in comparison to a heuristic decomposition approach. Our method achieved a success rate of over 95% in the microdissection and tissue yield was increased by 10–30%. We present a novel approach for constrained shape decomposition by demonstrating its advantages for the application in the microdissection of tissue samples. In comparison to the previous decomposition approach, the proposed method considerably increases the amount of successfully dissected tissue.

中文翻译：

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激光捕获显微切割的形状分解算法

在生物标志物发现和疾病分子表征的背景下，激光捕获显微切割是一种从复杂的异质组织样本中提取疾病特异性区域的高效方法。为了提取成功，这些区域必须满足一定的大小和形状限制，因此必须分解成可行的片段。我们将此约束形状分解问题建模为计算简单多边形的最佳可行分解。我们使用基于骨架的方法并提出了一个算法框架，该框架允许实现各种可行性标准以及优化目标。受我们应用程序的启发，我们考虑了不同的约束并检查了由此产生的碎片。与启发式分解方法相比，我们在肺组织样本上评估了我们的算法。我们的方法在显微切割中取得了超过 95% 的成功率，组织产量增加了 10-30%。我们通过展示其在组织样本显微切割中的应用优势，提出了一种约束形状分解的新方法。与以前的分解方法相比，所提出的方法大大增加了成功解剖组织的数量。