An Auxiliary Task for Learning Nuclei Segmentation in 3D Microscopy Images

Understand

Segmentation of cell nuclei in microscopy images is a prevalent necessity in cell biology.

• Especially for three-dimensional datasets, manual segmentation is prohibitively time-consuming, motivating the need for automated methods.
• Learning-based methods trained on pixel-wise ground-truth segmentations have been shown to yield state-of-the-art results on 2d benchmark image data of nuclei, yet a respective benchmark is missing for 3d image data.
• In this work, we perform a comparative evaluation of nuclei segmentation algorithms on a database of manually segmented 3d light microscopy volumes.