Addressing bias in manual segmentation of spheroid sprouting assays with U-Net

Angiogenesis research faces the issue of false-positive findings due to the manual analysis pipelines involved in many assays. For example, the spheroid sprouting assay, one of the most prominent in vitro angiogenesis models, is commonly based on manual segmentation of sprouts. In this study, we propose a method for mitigating subconscious or fraudulent bias caused by manual segmentation. This approach involves training a U-Net model on manual segmentations and using the readout of this U-Net model instead of the potentially biased original segmentations. Our hypothesis is that U-Net will mitigate any bias in the manual segmentations because this will impose only random noise during model training. We assessed this idea using a simulation study.

The training data comprised 1531 phase contrast images and manual segmentations from various spheroid sprouting assays. We randomly divided the images 1:1 into two groups: a fictitious intervention group and a control group. Bias was simulated exclusively in the intervention group. We simulated two adversarial scenarios: 1) removal of a single randomly selected sprout and 2) systematic shortening of all sprouts. For both scenarios, we compared the original segmentation, adversarial segmentation, and respective U-Net readouts. In the second step, we assessed the sensitivity of this approach to detect a true positive effect. We sampled multiple treatment and control groups with decreasing treatment effects based on unbiased ground truth segmentation.

This approach was able to mitigate bias in both adversarial scenarios. However, in both scenarios, U-Net detected the real treatment effects based on a comparison to the ground truth.

This method may prove useful for verifying positive findings in angiogenesis experiments with a manual analysis pipeline when full investigator masking has been neglected or is not feasible.