Faster R-CNN based microscopic cell detection

The automatic analysis of microscopic images is an important subject of medical image processing, of which the cell detection is an important part. However, owing to the different size and shape, as also as the adhesion among cells, detecting and locating cells accurately seems to be a very challeng...

Full description

Saved in:
Bibliographic Details
Published in2017 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) pp. 345 - 350
Main Authors Yang, Su, Fang, Bin, Tang, Wei, Wu, Xuegang, Qian, Jiye, Yang, Weibin
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2017
Subjects
cell detection
Electronic mail
Faster R-CNN
Feature extraction
Machine learning
microscopic images
Microscopy
Object detection
Proposals
RPN
White blood cells
Online AccessGet full text

Cover

More Information
Summary:The automatic analysis of microscopic images is an important subject of medical image processing, of which the cell detection is an important part. However, owing to the different size and shape, as also as the adhesion among cells, detecting and locating cells accurately seems to be a very challenging task. In this work, we investigate applying the Faster R-CNN, which has recently shown incredible performance on many public datasets, to cell detection. The Faster R-CNN contains both segmentation and classification. By training a Faster R-CNN model, a series of experiments are achieved. Experimental results show that the Faster R-CNN can detect almost all cells in a microscopic image. The proposed cell detector has improved detection performance, and it is easy-implemented and time-saving.
DOI:10.1109/SPAC.2017.8304302