Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

BMC Bioinformatics

Methodology Article

Maryann E Martone¹  Mark H Ellisman¹  Richard J Giuly¹ 

[1] Center for Research in Biological Systems, University of California, 9500 Gilman Dr., 92093, La Jolla, CA, USA;

关键词: True Positive Rate;    Automatic Segmentation;    Geodesic Active Contour;    Random Forest Classification;    Single Contour;   

DOI  :  10.1186/1471-2105-13-29

 received in 2011-08-26, accepted in 2012-02-09,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundWhile progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps.ResultsWe report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features.ConclusionsWe demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be possible to replace this with other texture identifiers, and we plan to explore this in future work.

【 授权许可】

CC BY   
© Giuly et al; licensee BioMed Central Ltd. 2012