Summary | Background | Methods | Results | Publications

 

 

Kinetochore microtubules and the associated plus-ends have been under intensive investigation in cell biology and molecular medicine. Though electron tomography provides new possibilities in imaging their high-resolution structures, the interpretation of the acquired data remains difficult because of the complex and cluttered cellular images. As a result, practical segmentation of the tomography volume has been dominated by manual operation, which is time consuming and subjective. In this research, we develop a model-based automated approach to extracting tubular objects and curvilinear structures from noisy volume images. In particular, we make the segmentation robust by systematically incorporating various prior models of the structures throughout image enhancement and segmentation. Our segmentation methods, regularized under the prior constraints, successfully extract fine biological structures such as microtubules and their plus-ends from highly complicated and noisy cellular environment.

Experimental results (click for sample video) demonstrate that our automated method produces results of microtubules and plus-ends comparable to manual segmentation but uses only a fraction of the time of the latter. The methodology developed in this work can also be applied to other segmentation tasks in electron tomography or other general areas. It is especially suitable for applications for which image quality is poor but there exist strong prior models.

 

 

Summary | Background | Methods | Results | Publications