Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders

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 cel...

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Veröffentlicht in:Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention Jg. 9351; S. 383
Hauptverfasser: Su, Hai, Xing, Fuyong, Kong, Xiangfei, Xie, Yuanpu, Zhang, Shaoting, Yang, Lin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Germany 01.10.2015
Schlagworte:
Algorithms
Brain Neoplasms - pathology
Cell Count
Cell Shape
Diagnosis, Computer-Assisted - methods
Humans
Image Processing, Computer-Assisted
Lung Neoplasms - pathology
Machine Learning
Reproducibility of Results
Sensitivity and Specificity
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Zusammenfassung: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.
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DOI:10.1007/978-3-319-24574-4_46