ArticlebioRxiv : the preprint server for biology2026
NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images.
Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Automating cellular organelle segmentation is key to increasing the throughput in electron microscopy (EM) and volume EM (vEM) workflows. Deep learning (DL) has accelerated this process, but model development has predominately centered on mitochondria, partly because of a scarcity of suitable training datasets for other features. Here, we crowdsourced the manual step of labeling nuclei and lipid droplets (LDs) from complex cellular EM images and trained Panoptic DeepLab (PDL) models on these large, heterogenous annotated datasets as well as on publicly available vEM datasets.
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