title: Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders.
abstract: Computer-aided diagnosis (CAD) is a promising tool for accurate and consistent diagnosis and prognosis. Cell detection and segmentation are essential steps for CAD. These tasks are challenging due to variations in cell shapes, touching cells, and cluttered background. In this paper, we present a cell detection and segmentation algorithm using the sparse reconstruction with trivial templates and a stacked denoising autoencoder (sDAE). The sparse reconstruction handles the shape variations by representing a testing patch as a linear combination of shapes in the learned dictionary. Trivial templates are used to model the touching parts. The sDAE, trained with the original data and their structured labels, is used for cell segmentation. To the best of our knowledge, this is the first study to apply sparse reconstruction and sDAE with structured labels for cell detection and segmentation. The proposed method is extensively tested on two data sets containing more than 3000 cells obtained from brain tumor and lung cancer images. Our algorithm achieves the best performance compared with other state of the arts.
authors: J. Crayton Pruitt Family Dept. of Biomedical Engineering, University of Florida, FL 32611.
authors: Hai
authors: H
authors: Su
authors: Department of Electrical and Computer Engineering, University of Florida, FL 32611.
authors: Fuyong
authors: F
authors: Xing
authors: J. Crayton Pruitt Family Dept. of Biomedical Engineering, University of Florida, FL 32611.
authors: Xiangfei
authors: X
authors: Kong
authors: J. Crayton Pruitt Family Dept. of Biomedical Engineering, University of Florida, FL 32611.
authors: Yuanpu
authors: Y
authors: Xie
authors: Department of Computer Science, University of North Carolina at Charlotte, NC 28223.
authors: Shaoting
authors: S
authors: Zhang
authors: J. Crayton Pruitt Family Dept. of Biomedical Engineering, University of Florida, FL 32611; Department of Electrical and Computer Engineering, University of Florida, FL 32611.
authors: Lin
authors: L
authors: Yang
mesh_terms: D000465:Algorithms; D001932:Brain Neoplasms / Q000473:pathology*; D002452:Cell Count; D048430:Cell Shape; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008175:Lung Neoplasms / Q000473:pathology*; D000069550:Machine Learning; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity