Rank-based Transformation for Image Contrast Adjustment Enhances Cell Segmentation

Proper image contrast adjustment without information loss is essential but challenging, often requiring experience or trial and error. Nonetheless, we expected that the multiple normalization steps embedded in deep neural networks would render external contrast adjustment redundant in modern cell segmentation tools--but that is not the case. We evaluate the impact of contrast preprocessing on three state-of-the-art segmentation tools: Cellpose, PlantSeg, and Ilastik. Contrast adjustment via the rank-based transformation (RBT) method requires no parameter tuning or prior knowledge. RBT computes pixel ranks and maps them across the full dynamic range, ensuring uniform contrast enhancement without information loss. It is particularly effective for underexposed images. Preprocessing with RBT leads to visibly improved segmentation in advanced tools like Cellpose. Our results falsify the claim that modern segmentation tools are insensitive to contrast preprocessing.