Robust Cell Detection and Segmentation in Histopathological Images Using Sparse Reconstruction and Stacked Denoising Autoencoders
Lung Neoplasms
Brain Neoplasms
Reproducibility of Results
Cell Count
02 engineering and technology
Sensitivity and Specificity
Machine Learning
03 medical and health sciences
0302 clinical medicine
Image Processing, Computer-Assisted
0202 electrical engineering, electronic engineering, information engineering
Humans
Diagnosis, Computer-Assisted
Cell Shape
Algorithms
DOI:
10.1007/978-3-319-24574-4_46
Publication Date:
2015-09-24T07:03:02Z
AUTHORS (6)
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.
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CITATIONS (38)
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