Abstract
Super Resolution (SR) microscopy leverages a variety of optical and computational techniques for overcoming the optical diffraction limit to acquire additional spatial details. However, added spatial details challenge existing segmentation tools. Confounding features include protein distributions that form membranes and boundaries, such as cellular and nuclear surfaces. We present a segmentation pipeline that retains the benefits provided by SR in surface separation while providing a tensor field to overcome these confounding features. The proposed technique leverages perceptual grouping to generate a tensor field that enables robust evolution of active contours despite ill-defined membrane boundaries.
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Acknowledgement
This work is funded in part by the National Institutes of Health/National Heart, Lung, and Blood Institute (NHLBI) #R01HL146745, the Cancer Prevention and Research Institute of Texas (CPRIT) #RR140013, the National Science Foundation I/UCRC BRAIN Center #1650566, and the National Institutes of Health Training Grant #T15LM007093.
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Li, J., Artur, C., Eriksen, J., Roysam, B., Mayerich, D. (2020). Segmenting Continuous but Sparsely-Labeled Structures in Super-Resolution Microscopy Using Perceptual Grouping. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12265. Springer, Cham. https://doi.org/10.1007/978-3-030-59722-1_14
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