Harnessing SSL for Segmentation in 3D Microscopy with Noisy Labels and Hard Patches

Segmentation in 3D microscopy is challenging due to hard (tosegment) patches, noisy labels resulting from the use of semi-automatedlabeling methods, image artifacts, off-target fluorescence, and lack of la-beled data because of high labeling effort. In this work, we introducea novel unified method to tackle these issues simultaneously in 3D mi-croscopy. First, we introduce µ3DINO, a 3D model pretrained on anultra-large multimodal dataset of over 2 million microscopy volumes.We then create µDivSeg, a segmentation pipeline that uses pretrainedweights to detect noisy labels and hard patches to guide and correct seg-mentation training. We evaluate our methods on a toy dataset and 4 real-world datasets from light-sheet and two-photon microscopy with a varietyof markers, in comparison to two state-of-the-art (SOTA) pipelines. Ourmethods outperform SOTA techniques on increasing levels of syntheticlabel perturbations and real-world data with diverse distributions.