title: Machine learning of hierarchical clustering to segment 2D and 3D images.
abstract: We aim to improve segmentation through the use of machine learning tools during region agglomeration. We propose an active learning approach for performing hierarchical agglomerative segmentation from superpixels. Our method combines multiple features at all scales of the agglomerative process, works for data with an arbitrary number of dimensions, and scales to very large datasets. We advocate the use of variation of information to measure segmentation accuracy, particularly in 3D electron microscopy (EM) images of neural tissue, and using this metric demonstrate an improvement over competing algorithms in EM and natural images.
authors: Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, Virginia, United States of America.
authors: Juan
authors: J
authors: Nunez-Iglesias
authors: Ryan
authors: R
authors: Kennedy
authors: Toufiq
authors: T
authors: Parag
authors: Jianbo
authors: J
authors: Shi
authors: Dmitri B
authors: DB
authors: Chklovskii
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D021621:Imaging, Three-Dimensional; D008854:Microscopy, Electron; D011336:Probability
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