Stefan Wernitznig1, Mariella Sele1, Martin Urschler2, Armin Zankel3, Peter Pölt3, F Claire Rind4, Gerd Leitinger5. 1. Institute of Cell Biology, Histology and Embryology, Research Unit Electron Microscopic Techniques, Medical University of Graz, Harrachgasse 21, 8010 Graz, Austria. 2. Ludwig Boltzmann Institute for Clinical Forensic Imaging, Universitätsplatz 4, 8010 Graz, Austria; Institute for Computer Graphics and Vision, BioTechMed-Graz, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria. 3. Institute for Electron Microscopy and Nanoanalysis, NAWI Graz, Graz University of Technology, Steyrergasse 17, 8010 Graz, Austria. 4. Newcastle University, Institute of Neuroscience, Framlington Place, NE2 4HH Newcastle upon Tyne, UK. 5. Institute of Cell Biology, Histology and Embryology, Research Unit Electron Microscopic Techniques, Medical University of Graz, Harrachgasse 21, 8010 Graz, Austria. Electronic address: gerd.leitinger@medunigraz.at.
Abstract
BACKGROUND: Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. NEW METHOD: The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. RESULTS: For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. COMPARISON WITH EXISTING METHODS: Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. CONCLUSION: Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.
BACKGROUND: Elucidating the anatomy of neuronal circuits and localizing the synaptic connections between neurons, can give us important insights in how the neuronal circuits work. We are using serial block-face scanning electron microscopy (SBEM) to investigate the anatomy of a collision detection circuit including the Lobula Giant Movement Detector (LGMD) neuron in the locust, Locusta migratoria. For this, thousands of serial electron micrographs are produced that allow us to trace the neuronal branching pattern. NEW METHOD: The reconstruction of neurons was previously done manually by drawing cell outlines of each cell in each image separately. This approach was very time consuming and troublesome. To make the process more efficient a new interactive software was developed. It uses the contrast between the neuron under investigation and its surrounding for semi-automatic segmentation. RESULTS: For segmentation the user sets starting regions manually and the algorithm automatically selects a volume within the neuron until the edges corresponding to the neuronal outline are reached. Internally the algorithm optimizes a 3D active contour segmentation model formulated as a cost function taking the SEM image edges into account. This reduced the reconstruction time, while staying close to the manual reference segmentation result. COMPARISON WITH EXISTING METHODS: Our algorithm is easy to use for a fast segmentation process, unlike previous methods it does not require image training nor an extended computing capacity. CONCLUSION: Our semi-automatic segmentation algorithm led to a dramatic reduction in processing time for the 3D-reconstruction of identified neurons.
Authors: Ali Shahbazi; Jeffery Kinnison; Rafael Vescovi; Ming Du; Robert Hill; Maximilian Joesch; Marc Takeno; Hongkui Zeng; Nuno Maçarico da Costa; Jaime Grutzendler; Narayanan Kasthuri; Walter J Scheirer Journal: Sci Rep Date: 2018-09-24 Impact factor: 4.379
Authors: Patricia Goggin; Elaine M L Ho; Helmut Gnaegi; Stuart Searle; Richard O C Oreffo; Philipp Schneider Journal: Bone Date: 2019-10-24 Impact factor: 4.398