Interactive Cell Segmentation Based on Active and Semi-Supervised Learning

Abstract

Automatic cell segmentation can hardly be flawless due to the complexity of image data particularly when time-lapse experiments last for a long time without biomarkers. To address this issue, we propose an interactive cell segmentation method by classifying feature-homogeneous superpixels into specific classes, which is guided by human interventions. Specifically, we propose to actively select the most informative superpixels by minimizing the expected prediction error which is upper bounded by the transductive Rademacher complexity, and then query for human annotations. After propagating the user-specified labels to the remaining unlabeled superpixels via an affinity graph, the error-prone superpixels are selected automatically and request for human verification on them; once erroneous segmentation is detected and subsequently corrected, the information is propagated efficiently over a gradually-augmented graph to un-labeled superpixels such that the analogous errors are fixed meanwhile. The correction propagation step is efficiently conducted by introducing a verification propagation matrix rather than rebuilding the affinity graph and re-performing the label propagation from the beginning. We repeat this procedure until most superpixels are classified into a specific category with high confidence. Experimental results performed on three types of cell populations validate that our interactive cell segmentation algorithm quickly reaches high quality results with minimal human interventions and is significantly more efficient than alternative methods, since the most informative samples are selected for human annotation/verification early.

Department(s)

Computer Science

Research Center/Lab(s)

Intelligent Systems Center

Keywords and Phrases

Artificial Intelligence; Cell Culture; Cell Proliferation; Cells; Cytology; Errors; Pixels; Supervised Learning; Active Learning; Cell Segmentation; Human Intervention; Label Propagation; Microscopy Image Analysis; Propagation Matrix; Rademacher Complexity; Semi-Supervised Learning; Image Segmentation; Cell Population; Error; Experimental Model; Human; Prediction; Algorithm; Cytology; Image Processing; Microscopy; Procedures; Supervised Machine Learning; Algorithms; Cytological Techniques; Humans; Image Processing; Computer-Assisted; Microscopy; Supervised Machine Learning; Verification Propagation

International Standard Serial Number (ISSN)

0278-0062

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Mar 2016

Link to Full Text

Share

 
COinS