Segmentation of Organoid Cultures Images Using Diffusion Networks with Triplet Loss.

The present research study explores the use of Diffusion Networks with Triplet Loss for the semantic segmentation of liver organoid cultures images. Since diffusion networks are generative, they encode more abstract and higher-level representations of the training images. In order to adapt such a generative architecture for image segmentation, we implemented triplet loss as its loss objective so that we encourage the diffused predictions to resemble the segmentation maps instead of the original images. The research question is whether the triplet loss is applicable to such an architecture and task, and whether diffusion networks with triplet loss are more performant, and trainable than previous supervised and self-supervised alternatives. This was tested by training the proposed model with a maximum of 500 images, evaluating its F1-score and comparing it to a supervised and self-supervised baseline. Our model (F1=83.7%) significantly outperformed the supervised baseline (F1=64.4%) but was outperformed by the self-supervised one (F1=85.0%). Nevertheless, our model showed greater robustness for typical images but low reliability, as it struggled with ambiguous inputs, causing a skewed distribution of the results and a lower mean score. It was also shown that the proposed model is more trainable than supervised approaches as it needed only 50 training samples to outperform the supervised baseline, which was trained with 114. However, it was less trainable than the self-supervised baseline, whereas it took our model over 200 training samples to match the accuracy of the self-supervised one (trained on 114).