Search Results for author:
Found 5 papers, 3 papers with code
Towards Interpretable Semantic Segmentation via Gradient-weighted Class Activation Mapping
Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks.
Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy
Accurate detection and segmentation of cell nuclei in volumetric (3D) fluorescence microscopy datasets is an important step in many biomedical research projects.
Cell Detection with Star-convex Polygons
Automatic detection and segmentation of cells and nuclei in microscopy images is important for many biological applications.
Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks
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.
Crowd Sourcing Image Segmentation with iaSTAPLE
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.
