Joseph T Vecchi1, Sean Mullan2, Josue A Lopez1, Marlan R Hansen1, Milan Sonka2, Amy Lee3. 1. Departments of Molecular Physiology and Biophysics, Otolaryngology Head-Neck Surgery, and Neurology, Iowa Neuroscience Institute, Pappajohn Biomedical Institute, University of Iowa, 51 Newton Rd., Iowa City, IA 52242, USA. 2. Iowa Institute for Biomedical Imaging, Electrical and Computer Engineering, University of Iowa, 169 Newton Rd., Iowa City, IA 52242, USA. 3. Dept of Neuroscience, University of Texas-Austin, 100 E. 24th St., Austin, TX 78712, USA. Electronic address: amy.lee1@austin.utexas.edu.
Abstract
BACKGROUND: During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro. New Method: NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure. RESULTS: In this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurons cultured from control and genetically modified mouse strains. Comparison with Existing Methods: Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth. CONCLUSIONS: NeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay.
BACKGROUND: During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro. New Method: NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure. RESULTS: In this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurons cultured from control and genetically modified mouse strains. Comparison with Existing Methods: Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth. CONCLUSIONS: NeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay.
Authors: Christopher G Langhammer; Michelle L Previtera; Eric S Sweet; Simranjeet S Sran; Maxine Chen; Bonnie L Firestein Journal: Cytometry A Date: 2010-12 Impact factor: 4.355
Authors: Priya Srikanth; Valentina N Lagomarsino; Richard V Pearse; Meichen Liao; Sulagna Ghosh; Ralda Nehme; Nicholas Seyfried; Kevin Eggan; Tracy L Young-Pearse Journal: Transl Psychiatry Date: 2018-11-08 Impact factor: 6.222