Exploring the Value of Weakly-Supervised Deep Learning Approaches for Artefact Segmentation in Brightfield Microscopy Images

Abstract

Brightfield microscopy is of great importance as it offers researchers a relatively simple way to quantify cellular experiments. However, brightfield images often contain a variety of artefacts that should be segmented and thereafter neutralized so that they would not affect the quantitative measurements of cellular experiments. While fully-supervised deep learning models offer state-of-the-art performance in most segmentation tasks in computer vision, it is laborious to acquire the pixel-level labels needed to train these models. Alternatively, segmentation tasks can also be solved using more time- and cost-effective weakly-supervised deep learning models that use image-level labels for training. In this thesis, we compare the performances of fully- (e.g., U-Net) and weakly-supervised approaches (e.g., Score-CAM) to determine whether weakly-supervised approaches could be used as a cheaper but still well-performing solution for segmenting artefacts in brightfield images. Six separate experiments with various fully- and weakly-supervised approaches, image datasets and method ensembles are carried out. The results of the experiments showed that with the number of images and labels currently available, none of the weakly-supervised approaches were able to replicate the performance of the baseline fully-supervised approach. However, some of the weakly supervised approaches, like the combined Score-CAM and U-Net approach, showed promising segmentation results. Moreover, the same approach also showed better generalizability on an unseen dataset than the baseline fully-supervised approach. Future work is required to find the amount of weak supervision signal needed to match the performance of the fully-supervised approaches.