Feng, Linqing Zhao, Ting Kim, Jinhyun 2024-01-20T14:32:45Z 2024-01-20T14:32:45Z 2022-01-10 2012-06-15 1367-4803 https://pubs.kist.re.kr/handle/201004/129144 Motivation: A new technique, mammalian green fluorescence protein (GFP) reconstitution across synaptic partners (mGRASP), enables mapping mammalian synaptic connectivity with light microscopy. To characterize the locations and distribution of synapses in complex neuronal networks visualized by mGRASP, it is essential to detect mGRASP fluorescence signals with high accuracy. Results: We developed a fully automatic method for detecting mGRASP-labeled synapse puncta. By modeling each punctum as a Gaussian distribution, our method enables accurate detection even when puncta of varying size and shape partially overlap. The method consists of three stages: blob detection by global thresholding; blob separation by watershed; and punctum modeling by a variational Bayesian Gaussian mixture models. Extensive testing shows that the three-stage method improved detection accuracy markedly, and especially reduces under-segmentation. The method provides a goodness-of-fit score for each detected punctum, allowing efficient error detection. We applied this advantage to also develop an efficient interactive method for correcting errors. English OXFORD UNIV PRESS LIVE CELLS TRACKING MODELS 3D RECONSTRUCTION SEGMENTATION PARTICLES NUCLEI Improved synapse detection for mGRASP-assisted brain connectivity mapping Article 10.1093/bioinformatics/bts221 1 BIOINFORMATICS, v.28, no.12, pp.I25 - I31 BIOINFORMATICS 28 12 I25 I31 scie scopus 000305419800004 2-s2.0-84863542265 Biochemical Research Methods Biotechnology & Applied Microbiology Computer Science, Interdisciplinary Applications Mathematical & Computational Biology Statistics & Probability Biochemistry & Molecular Biology Biotechnology & Applied Microbiology Computer Science Mathematical & Computational Biology Mathematics Article; Proceedings Paper LIVE CELLS TRACKING MODELS 3D RECONSTRUCTION SEGMENTATION PARTICLES NUCLEI