Learning to boost filamentary structure segmentation

Lin Gu, Li Cheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

31 Citations (Scopus)


The challenging problem of filamentary structure segmentation has a broad range of applications in biological and medical fields. A critical yet challenging issue remains on how to detect and restore the small filamentary fragments from backgrounds: The small fragments are of diverse shapes and appearances, meanwhile the backgrounds could be cluttered and ambiguous. Focusing on this issue, this paper proposes an iterative two-step learning-based approach to boost the performance based on a base segmenter arbitrarily chosen from a number of existing segmenters: We start with an initial partial segmentation where the filamentary structure obtained is of high confidence based on this existing segmenter. We also define a scanning horizon as epsilon balls centred around the partial segmentation result. Step one of our approach centers on a data-driven latent classification tree model to detect the filamentary fragments. This model is learned via a training process, where a large number of distinct local figure/background separation scenarios are established and geometrically organized into a tree structure. Step two spatially restores the isolated fragments back to the current partial segmentation, which is accomplished by means of completion fields and matting. Both steps are then alternated with the growth of partial segmentation result, until the input image space is entirely explored. Our approach is rather generic and can be easily augmented to a wide range of existing supervised/unsupervised segmenters to produce an improved result. This has been empirically verified on specific filamentary structure segmentation tasks: retinal blood vessel segmentation as well as neuronal segmentations, where noticeable improvement has been shown over the original state-of-the-arts.
Original languageEnglish
Title of host publication2015 International Conference on Computer Vision, ICCV 2015
Number of pages9
Volume2015 International Conference on Computer Vision, ICCV 2015
ISBN (Electronic)9781467383912
Publication statusPublished - 17 Feb 2015
Externally publishedYes
Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
Duration: 11 Dec 201518 Dec 2015


Conference15th IEEE International Conference on Computer Vision, ICCV 2015

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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