2026-07-20T11:45:54Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:12802 2024-03-06T10:50:55Z ddc:004 open_access

Shape Decomposition Algorithms for Laser Capture Microdissection Selbach, Leonie Kowalski, Tobias Gerwert, Klaus Buchin, Maike Mosig, Axel Laser capture microdissection shape decomposition skeletonization 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. These regions have to be decomposed into feasible fragments as they have to satisfy certain constraints in size and morphology for the extraction to be successful. 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. Furthermore, we apply our method to lung tissue samples and show its advantages in comparison to a heuristic decomposition approach. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Leonie Selbach and Tobias Kowalski and Klaus Gerwert and Maike Buchin and Axel Mosig 2020 Is Part Of LIPIcs, Volume 172, 20th International Workshop on Algorithms in Bioinformatics (WABI 2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.WABI.2020.13 urn:nbn:de:0030-drops-128020 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.WABI.2020.13 eng https://creativecommons.org/licenses/by/3.0/legalcode