ROBUSTNESS BENCHMARK DATASETS FOR SEMANTIC SEGMENTATION OF FLUORESCENCE IMAGES REVISED

We have developed three datasets, referred to as ER-C, Mito-C and Nucleus-C, respectively, for benchmarking robustness of DNN models against corruptions and adversarial attacks in semantic segmentation of fluorescence microscopy images. Degraded images in these three datasets are synthesized from raw images along with their manually annotated segmentation labels in the ER, Mito, and Nucleus datasets [1,2], respectively. They are synthesized with controlled corruptions and adversarial attacks. We uploaded an initial version in June 2022 [3] (https://ieee-dataport.org/documents/robustness-benchmark-datasets-semantic-segmentation-fluorescence-images), and this is an updated version that mainly adjusts the pure Gaussian noise in the original version to Gaussian Poisson mixed noise. [1] Y. Luo, Y. Guo, W. Li, G. Liu, and G. Yang. Fluorescence Microscopy Image Datasets for Deep Learning Segmentation of Intracellular Orgenelle Networks [Online] Available: https://dx.doi.org/10.21227/t2he-zn97.[2] J. C. Caicedo et al., "Nucleus segmentation across imaging experiments: the 2018 Data Science Bowl," Nature Methods, vol. 16, no. 12, pp. 1247-1253, 2019.[3] Liqun Zhong, Ge Yang, Lingrui Li, June 26, 2022, "Robustness Benchmark Datasets for Semantic Segmentation of Fluorescence Images", IEEE Dataport, doi: https://dx.doi.org/10.21227/kxay-5y38.