Fast Cell Segmentation Using Scalable Sparse Manifold Learning and Affine Transform-Approximated Active Contour

• Published in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 Vol. 9351; pp. 332 - 339
• Main Authors: Xing, Fuyong, Yang, Lin
• Format: Book Chapter Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2015
• Series: Lecture Notes in Computer Science
• Subjects: Active Contour; Active Contour Model; Algorithms; Cell Separation - methods; Convolutional Neural Network; Efficiency; Humans; Machine Learning; Manifold Learn; Neuroendocrine Tumors - pathology; Pancreatic Neoplasms - pathology; Reproducibility of Results; Sensitivity and Specificity; Sparse Code
• ISBN: 9783319245737; 3319245732
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-24574-4_40

Efficient and effective cell segmentation of neuroendocrine tumor (NET) in whole slide scanned images is a difficult task due to a large number of cells. The weak or misleading cell boundaries also present significant challenges. In this paper, we propose a fast, high throughput cell segmentation algorithm by combining top-down shape models and bottom-up image appearance information. A scalable sparse manifold learning method is proposed to model multiple subpopulations of different cell shape priors. Followed by a shape clustering on the manifold, a novel affine transform-approximated active contour model is derived to deform contours without solving a large amount of computationally-expensive Euler-Lagrange equations, and thus dramatically reduces the computational time. To the best of our knowledge, this is the first report of a high throughput cell segmentation algorithm for whole slide scanned pathology specimens using manifold learning to accelerate active contour models. The proposed approach is tested using 12 NET images, and the comparative experiments with the state of the arts demonstrate its superior performance in terms of both efficiency and effectiveness.