The visualized comparison of robustness of ILEE and other algorithms by segmentation accuracy. The raw demonstration image of Fig. 4 a is added with a series of gaussian noise (μ = 0, σ as variable). The binary images are computed by selected algorithms including ILEE and compared with manually portraited ground truth, and pixels of match, false-positive, and false-negative are presented. By visual observation, all of the algorithms are stable when the σ of noise is within 100 (3–4% of max of dynamic range); three of the better performed algorithm, ILEE, MGT, and Li, remain accurate, among which ILEE has the best coverage of ground truth. When σ of noise is higher than 100, all algorithms become visibly unstable. While MGT and Li tend to have single pixel errors while ILEE tends to result in block-shaped errors that are less in number and mimics the thickness of the cytoskeleton in shape, which indicates that the indices derived from ILEE are potentially more accurate at high noise conditions.