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• W4310421807 abstract "Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical application is severely limited by computational resources. In this work, we propose and investigate an alternative spatial data representation based on cell segmentation data for direct training of CNNs.We introduce and analyze the properties of Cell2Grid, an algorithm that generates compact images from cell segmentation data by placing individual cells into a low-resolution grid and resolves possible cell conflicts. For evaluation, we present a case study on colorectal cancer relapse prediction using fluorescent multiplex immunohistochemistry images.We could generate Cell2Grid images at 5-μm resolution that were 100 times smaller than the original ones. Cell features, such as phenotype counts and nearest-neighbor cell distances, remain similar to those of original cell segmentation tables ( p<0.0001 ). These images could be directly fed to a CNN for predicting colon cancer relapse. Our experiments showed that test set error rate was reduced by 25% compared with CNNs trained on images rescaled to 5μm with bilinear interpolation. Compared with images at 1-μm resolution (bilinear rescaling), our method reduced CNN training time by 85%.Cell2Grid is an efficient spatial data representation algorithm that enables the use of conventional CNNs on cell segmentation data. Its cell-based representation additionally opens a door for simplified model interpretation and synthetic image generation." @default.
• W4310421807 created "2022-12-10" @default.
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• W4310421807 date "2022-11-30" @default.
• W4310421807 modified "2023-10-18" @default.
• W4310421807 title "Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data" @default.
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• W4310421807 doi "https://doi.org/10.1117/1.jmi.9.6.067501" @default.
• W4310421807 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36466076" @default.
• W4310421807 hasPublicationYear "2022" @default.
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