Iterative multi-path tracking for video and volume segmentation with sparse point supervision

Lejeune, Laurent Georges Pascal; Grossrieder, Jan; Sznitman, Raphael (2018). Iterative multi-path tracking for video and volume segmentation with sparse point supervision. Medical image analysis, 50, pp. 65-81. Elsevier 10.1016/j.media.2018.08.007

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Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however to aggregate such datasets, as the time and monetary cost associated with collecting extensive annotations is extremely high. This is particularly the case for generating precise pixel-wise annotations in video and volumetric image data. To this end, this work presents a novel framework to produce pixel-wise segmentations using minimal supervision. Our method relies on 2D point supervision, whereby a single 2D location within an object of interest is provided on each image of the data. Our method then estimates the object appearance in a semi-supervised fashion by learning object-image-specific features and by using these in a semi-supervised learning framework. Our object model is then used in a graph-based optimization problem that takes into account all provided locations and the image data in order to infer the complete pixel-wise segmentation. In practice, we solve this optimally as a tracking problem using a K-shortest path approach. Both the object model and segmentation are then refined iteratively to further improve the final segmentation. We show that by collecting 2D locations using a gaze tracker, our approach can provide state-of-the-art segmentations on a range of objects and image modalities (video and 3D volumes), and that these can then be used to train supervised machine learning classifiers.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory

UniBE Contributor:

Lejeune, Laurent Georges Pascal; Grossrieder, Jan and Sznitman, Raphael

Subjects:

000 Computer science, knowledge & systems > 050 Magazines, journals & serials

ISSN:

1361-8415

Publisher:

Elsevier

Projects:

[1212] Eye Labeling for Medical Image Data

Language:

English

Submitter:

Laurent Georges Pascal Lejeune

Date Deposited:

06 Sep 2018 08:38

Last Modified:

18 May 2020 11:34

Publisher DOI:

10.1016/j.media.2018.08.007

BORIS DOI:

10.7892/boris.119851

URI:

https://boris.unibe.ch/id/eprint/119851

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