title: Large-scale localization of touching somas from 3D images using density-peak clustering.
abstract: BACKGROUND
  Soma localization is an important step in computational neuroscience to map neuronal circuits. However, locating somas from large-scale and complicated datasets is challenging. The challenges primarily originate from the dense distribution of somas, the diversity of soma sizes and the inhomogeneity of image contrast.
  
  
  RESULTS
  We proposed a novel localization method based on density-peak clustering. In this method, we introduced two quantities (the local density ρ of each voxel and its minimum distance δ from voxels of higher density) to describe the soma imaging signal, and developed an automatic algorithm to identify the soma positions from the feature space (ρ, δ). Compared with other methods focused on high local density, our method allowed the soma center to be characterized by high local density and large minimum distance. The simulation results indicated that our method had a strong ability to locate the densely positioned somas and strong robustness of the key parameter for the localization. From the analysis of the experimental datasets, we demonstrated that our method was effective at locating somas from large-scale and complicated datasets, and was superior to current state-of-the-art methods for the localization of densely positioned somas.
  
  
  CONCLUSIONS
  Our method effectively located somas from large-scale and complicated datasets. Furthermore, we demonstrated the strong robustness of the key parameter for the localization and its effectiveness at a low signal-to-noise ratio (SNR) level. Thus, the method provides an effective tool for the neuroscience community to quantify the spatial distribution of neurons and the morphologies of somas.
authors.affiliation: School of Mathematics and Statistics, Huazhong University of Science and Technology, 1037 Luoyu Rd, Building of Science - 715, Wuhan, 430074, China.|Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.|MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
authors.forename: Shenghua
authors.initials: S
authors.lastname: Cheng
authors.affiliation: Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.|MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.|School of Mathematics and Statistics, Hubei University of Education, Wuhan, 430205, China.
authors.forename: Tingwei
authors.initials: T
authors.lastname: Quan
authors.affiliation: School of Mathematics and Statistics, Huazhong University of Science and Technology, 1037 Luoyu Rd, Building of Science - 715, Wuhan, 430074, China. liuxiaomao2001@outlook.com.
authors.forename: Xiaomao
authors.initials: X
authors.lastname: Liu
authors.affiliation: Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, 430074, China.|MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
authors.forename: Shaoqun
authors.initials: S
authors.lastname: Zeng
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D021621:Imaging, Three-Dimensional / Q000379:methods*; D051379:Mice; D009474:Neurons / Q000166:cytology*; D059629:Signal-To-Noise Ratio
keywords: Density-peak clustering; Optical microscopic image; Touching soma localization