Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks.
Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects.
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to.
In an evaluation on a light microscopy dataset containing more than 5000 membrane labeled epithelial cells of a fly wing, we show that iaSTAPLE outperforms STAPLE in terms of segmentation accuracy as well as in terms of the accuracy of estimated crowd worker performance levels, and is able to correctly segment 99% of all cells when compared to expert segmentations.