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Benchmarking the Cell Image Segmentation Models Robustness under the Microscope Optical Aberrations
April 15, 2024, 4:45 a.m. | Boyuan Peng, Jiaju Chen, Qihui Ye, Minjiang Chen, Peiwu Qin, Chenggang Yan, Dongmei Yu, Zhenglin Chen
cs.CV updates on arXiv.org arxiv.org
Abstract: Cell segmentation is essential in biomedical research for analyzing cellular morphology and behavior. Deep learning methods, particularly convolutional neural networks (CNNs), have revolutionized cell segmentation by extracting intricate features from images. However, the robustness of these methods under microscope optical aberrations remains a critical challenge. This study comprehensively evaluates the performance of cell instance segmentation models under simulated aberration conditions using the DynamicNuclearNet (DNN) and LIVECell datasets. Aberrations, including Astigmatism, Coma, Spherical, and Trefoil, were …
abstract arxiv behavior benchmarking biomedical cellular cnns convolutional neural networks cs.cv deep learning eess.iv features however image images networks neural networks optical physics.bio-ph research robustness segmentation type
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