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. 2024 Feb 24;14:4489. doi: 10.1038/s41598-024-54993-y

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© The Author(s) 2024

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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(Right) Binary map of cell colony area calculated using morphological segmentation. This map is used to reduce the presence of background area when taking image patches. (Center) Example of a large-scale (1325 $\times$ 1123) multi-label image containing areas of each of the individual four classes. (Left) 128 $\times$ 128 patches of images and their corresponding locations within the larger image (from top to bottom: Dense, Debris, Spread, Differentiated). The high background to foreground ratio makes taking random patches from within the binary map of the colony a crucial step in providing relevant information to the model.