Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation
Ronell Sicat, Markus Hadwiger, Niloy J. Mitra
short paper EUROGRAPHICS 2013


Volume segmentation is an integral data analysis tool in experimental science. For example, in neuroscience, analysis of 3D volumes of neural structures from electron microscopy data is a key analysis step. Despite advances in computational methods, experts still prefer to manually proofread and correct the automatic segmentation outputs. Such corrections are often annotated at the level of data slices in order to minimize distortion artifacts and effectively handle the massive data volumes. In absence of crucial global context in 3D, such a workflow remains tedious, time consuming, and error prone. In this paper, we present a simple graph-based abstraction for segmentation volumes leading to an interactive proofreading tool making the process simpler, faster, and intuitive. Starting from an initial volume segmentation, we first construct a graph abstraction and then use it to identify potential problematic regions for the user to investigate and correct spurious segmentations, if identified. We also use the graph to suggest automatic corrections, thus drastically simplifying the proofreading effort. We implemented the proofreading tool as an Avizo c plugin and evaluated the method on complex real-world use cases.



AUTHOR = "Ronell Sicat and Markus Hadwiger and Niloy J. Mitra",
title = "Graph Abstraction for Simplified Proofreading of Slice-based Volume Segmentation",
booktitle = "EUROGRAPHICS Short Paper", 
YEAR = "2013",


We thank Verena Kaynig for her help and the dataset, and our collaborators at the Harvard Center for Brain Science.

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