Automated detection of potential artifacts in machine learning based bio-image segmentation

Image segmentation algorithms, while powerful, are inherently prone to artifacts, making perfect segmentation theoretically and practically impossible. To address this challenge, we propose an automated artifact identification scheme for posterior rapid manual re-correction. Hereby, our contribution is twofold: We extend our previous work, delivering polynomial defenses (PDs). These defenses mimic noise distributions that, when removed from the training images, significantly improve segmentation quality. In practice, we perturb unseen images with PDs and demonstrate that the resulting segmentation differences achieve superior precision in artifact detection compared to traditional Gaussian and Poisson noise perturbations. This automated guidance is our essential contribution. Beyond improving the reliability of image-processing outputs, our approach provides a valuable tool for enhancing manually segmented training datasets. Hereby, the automated guidance massively decreases manual cross-checking time.