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- W2805655379 abstract "Segmentation of cell nuclei is crucial for analyzing microscopy image data and understanding cellular processes. We performed a comparison of different approaches for segmentation of cell nuclei in difficult tissue microscopy image data of glioblastoma cells. We investigated nine different methods comprising thresholding, deformable models, region growing, unsupervised learning, and supervised learning using random forest. In addition, we studied two deep learning methods including a novel deep neural network based on atrous spatial pyramid pooling (ASPP), which was not yet used for cell segmentation. We used pixel-based and object-based performance metrics. The best results were obtained by machine learning methods, where deep learning models (Accuracy 91.7% and Dice 0.925) exceeded random forest classifiers (Accuracy 90.4 % and Dice 0.914). Using a deep neural network with an ASPP yielded a slightly lower segmentation performance, but was most effective in splitting merged objects." @default.
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- W2805655379 date "2018-04-01" @default.
- W2805655379 modified "2023-10-16" @default.
- W2805655379 title "Comparison of segmentation methods for tissue microscopy images of glioblastoma cells" @default.
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- W2805655379 doi "https://doi.org/10.1109/isbi.2018.8363601" @default.
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