%0 Report %1 0654958d6f3145a8a539505509ffb757 %A Presberger, Jannik %A Keshara, Rashmiparvathi %A Stein, David %A Kim, Yung Hae %A Grapin-Botton, Anne %A Andres, Bjoern %D 2024 %K FIS_scads imported topic_visualcomputing xack %R 10.48550/arXiv.2403.13376 %T Correlation Clustering of Organoid Images. %X In biological and medical research, scientists now routinely acquire microscopy images of hundreds of morphologically heterogeneous organoids and are then faced with the task of finding patterns in the image collection, i.e., subsets of organoids that appear similar and potentially represent the same morphological class. We adopt models and algorithms for correlating organoid images, i.e., for quantifying the similarity in appearance and geometry of the organoids they depict, and for clustering organoid images by consolidating conflicting correlations. For correlating organoid images, we adopt and compare two alternatives, a partial quadratic assignment problem and a twin network. For clustering organoid images, we employ the correlation clustering problem. Empirically, we learn the parameters of these models, infer a clustering of organoid images, and quantify the accuracy of the inferred clusters, with respect to a training set and a test set we contribute of state-of-the-art light microscopy images of organoids clustered manually by biologists.

@techreport{0654958d6f3145a8a539505509ffb757, abstract = {In biological and medical research, scientists now routinely acquire microscopy images of hundreds of morphologically heterogeneous organoids and are then faced with the task of finding patterns in the image collection, i.e., subsets of organoids that appear similar and potentially represent the same morphological class. We adopt models and algorithms for correlating organoid images, i.e., for quantifying the similarity in appearance and geometry of the organoids they depict, and for clustering organoid images by consolidating conflicting correlations. For correlating organoid images, we adopt and compare two alternatives, a partial quadratic assignment problem and a twin network. For clustering organoid images, we employ the correlation clustering problem. Empirically, we learn the parameters of these models, infer a clustering of organoid images, and quantify the accuracy of the inferred clusters, with respect to a training set and a test set we contribute of state-of-the-art light microscopy images of organoids clustered manually by biologists. }, added-at = {2024-11-28T16:27:18.000+0100}, author = {Presberger, Jannik and Keshara, Rashmiparvathi and Stein, David and Kim, {Yung Hae} and Grapin-Botton, Anne and Andres, Bjoern}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2f9d86b23e99e9ea095d964ea707b6d95/scadsfct}, doi = {10.48550/arXiv.2403.13376}, interhash = {ea24ba7e61812e9919610ed31ad72bec}, intrahash = {f9d86b23e99e9ea095d964ea707b6d95}, keywords = {FIS_scads imported topic_visualcomputing xack}, language = {English}, month = mar, note = {DBLP License: DBLP's bibliographic metadata records provided through http://dblp.org/ are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.}, timestamp = {2025-07-29T10:29:54.000+0200}, title = {Correlation Clustering of Organoid Images.}, type = {WorkingPaper}, year = 2024 }