title: High-throughput histopathological image analysis via robust cell segmentation and hashing.
abstract: Computer-aided diagnosis of histopathological images usually requires to examine all cells for accurate diagnosis. Traditional computational methods may have efficiency issues when performing cell-level analysis. In this paper, we propose a robust and scalable solution to enable such analysis in a real-time fashion. Specifically, a robust segmentation method is developed to delineate cells accurately using Gaussian-based hierarchical voting and repulsive balloon model. A large-scale image retrieval approach is also designed to examine and classify each cell of a testing image by comparing it with a massive database, e.g., half-million cells extracted from the training dataset. We evaluate this proposed framework on a challenging and important clinical use case, i.e., differentiation of two types of lung cancers (the adenocarcinoma and squamous carcinoma), using thousands of lung microscopic tissue images extracted from hundreds of patients. Our method has achieved promising accuracy and running time by searching among half-million cells .
authors.affiliation: Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA.
authors.forename: Xiaofan
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Fuyong
authors.initials: F
authors.lastname: Xing
authors.affiliation: Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Hai
authors.initials: H
authors.lastname: Su
authors.affiliation: Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA; Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA.
authors.forename: Lin
authors.initials: L
authors.lastname: Yang
authors.affiliation: Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA. Electronic address: rutgers.shaoting@gmail.com.
authors.forename: Shaoting
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000230:Adenocarcinoma / Q000473:pathology; D002294:Carcinoma, Squamous Cell / Q000473:pathology; D058948:Cell Tracking / Q000379:methods*; D003937:Diagnosis, Differential; D057166:High-Throughput Screening Assays / Q000379:methods; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008175:Lung Neoplasms / Q000473:pathology*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Cell segmentation; Hashing; Histopathological image analysis; Image retrieval; Large-scale