Fig. 2

Probabilistic interactive segmentation improves mitochondrial segmentation accuracy by refining uncertain regions through user feedback. (A) Segmentation results from the Lucchi++ and skeletal muscle datasets, comparing the performance of the standard U-Net model with the probabilistic interactive segmentation approach. Both models are pre-trained on the CEM500k dataset. For the Lucchi++ dataset, the gold standard segmentation mask is derived from the expert-labeled masks included in the dataset, while for the skeletal muscle dataset, it is based on manually segmented masks. (B) Visualization demonstrates how user feedback during interactive segmentation progressively enhances segmentation accuracy. Initial prediction errors are corrected with positive (blue) and negative (red) feedback clicks. The prediction errors, represented as differences between the ground truth and the model predictions, are highlighted in the uncertainty map to guide further refinements.