title: A two-layer structure prediction framework for microscopy cell detection.
abstract: The task of microscopy cell detection is of great biological and clinical importance. However, existing algorithms for microscopy cell detection usually ignore the large variations of cells and only focus on the shape feature/descriptor design. Here we propose a new two-layer model for cell centre detection by a two-layer structure prediction framework, which is respectively built on classification for the cell centres implicitly using rich appearances and contextual information and explicit structural information for the cells. Experimental results demonstrate the efficiency and effectiveness of the proposed method over competing state-of-the-art methods, providing a viable alternative for microscopy cell detection.
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, China; Microsoft Research, Beijing, China. Electronic address: xuyan04@gmail.com.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, China. Electronic address: wuweiying18@gmail.com.
authors.forename: Weiying
authors.initials: W
authors.lastname: Wu
authors.affiliation: Microsoft Research, Beijing, China. Electronic address: echang@microsoft.com.
authors.forename: Eric I-Chao
authors.initials: EI
authors.lastname: Chang
authors.affiliation: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, USA. Electronic address: dchen@cse.nd.edu.
authors.forename: Danny
authors.initials: D
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, USA. Electronic address: jmu@nd.edu.
authors.forename: Jian
authors.initials: J
authors.lastname: Mu
authors.affiliation: Cancer Immunotherapeutics and Tumor Immunology (CITI), Duarte, CA, USA. Electronic address: plee@coh.org.
authors.forename: Peter P
authors.initials: PP
authors.lastname: Lee
authors.affiliation: Cancer Immunotherapeutics and Tumor Immunology (CITI), Duarte, CA, USA. Electronic address: kblenman@cinci.rr.com.
authors.forename: Kim R M
authors.initials: KR
authors.lastname: Blenman
authors.affiliation: Department of Cognitive Science, University of California, San Diego, CA, USA; Department of Computer and Engineering, University of California, San Diego, CA, USA. Electronic address: zhuowen.tu@gmail.com.
authors.forename: Zhuowen
authors.initials: Z
authors.lastname: Tu
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000473:pathology*; D058948:Cell Tracking / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008198:Lymph Nodes / Q000473:pathology*; D008207:Lymphatic Metastasis; D000069550:Machine Learning; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Computer vision; Layered models; Machine learning; Microscopy cell detection; Structural prediction