A biosegmentation benchmark for evaluation of bioimage analysis methods.
BACKGROUND
We present a biosegmentation benchmark that includes infrastructure, datasets with associated ground truth, and validation methods for biological image analysis. The primary motivation for creating this resource comes from the fact that it is very difficult, if not impossible, for an end-user to choose from a wide range of segmentation methods available in the literature for a particular bioimaging problem. No single algorithm is likely to be equally effective on diverse set of images and each method has its own strengths and limitations. We hope that our benchmark resource would be of considerable help to both the bioimaging researchers looking for novel image processing methods and image processing researchers exploring application of their methods to biology.


RESULTS
Our benchmark consists of different classes of images and ground truth data, ranging in scale from subcellular, cellular to tissue level, each of which pose their own set of challenges to image analysis. The associated ground truth data can be used to evaluate the effectiveness of different methods, to improve methods and to compare results. Standard evaluation methods and some analysis tools are integrated into a database framework that is available online at http://bioimage.ucsb.edu/biosegmentation/.


CONCLUSIONS
This online benchmark will facilitate integration and comparison of image analysis methods for bioimages. While the primary focus is on biological images, we believe that the dataset and infrastructure will be of interest to researchers and developers working with biological image analysis, image segmentation and object tracking in general.
Drelie Gelasca
Elisa
E
Electrical and Computer Engineering Department, Center for Bio-Image Informatics, University of California Santa Barbara, CA 93106, USA. elisa.drelie@ieee.org
Obara
Boguslaw
B
Fedorov
Dmitry
D
Kvilekval
Kristian
K
Manjunath
Bs
B
D000465:Algorithms; D019985:Benchmarking / Q000379:methods; D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D016208:Databases, Factual; D007089:Image Enhancement; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods