2019/08/02 by Henrik Skibbe, Akiya Watakabe, Skibbe, Henrik +23 · 14 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Medicine · #Algorithm #Artificial intelligence #Cell Image Analysis Techniques #Computer science #Computer vision #Convolutional neural network #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Electrical engineering #Geology #Image (mathematics) #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Marmoset #Medical Image Segmentation Techniques #Neurons and Cognition (q-bio.NC) #Noise (video) #Optical Imaging and Spectroscopy Techniques #Pattern recognition (psychology) #Pipeline (software) #Projection (relational algebra) #SIGNAL (programming language) #Segmentation #cs.LG #eess.IV #electronic engineering #information engineering #q-bio.NC #stat.ML
paper · pdf · doi:10.48550/arxiv.1908.00876
published in arXiv (Cornell University) (Cornell University)
arxiv created 2019/08/02 · openalex publication_date 2019/08/02 · arxiv updated 2019/08/05 · openalex created_date 2019/08/13 · openalex updated_date 2026/07/30
Understanding the connectivity in the brain is an important prerequisite for understanding how the brain processes information. In the Brain/MINDS project, a connectivity study on marmoset brains uses two-photon microscopy fluorescence images of axonal projections to collect the neuron connectivity from defined brain regions at the mesoscopic scale. The processing of the images requires the detection and segmentation of the axonal tracer signal. The objective is to detect as much tracer signal as possible while not misclassifying other background structures as the signal. This can be challenging because of imaging noise, a cluttered image background, distortions or varying image contrast cause problems. We are developing MarmoNet, a pipeline that processes and analyzes tracer image data of the common marmoset brain. The pipeline incorporates state-of-the-art machine learning techniques based on artificial convolutional neural networks (CNN) and image registration techniques to extract and map all relevant information in a robust manner. The pipeline processes new images in a fully automated way. This report introduces the current state of the tracer signal analysis part of the pipeline.