Fast graph-based medical image segmentation with expert guided statistical information Conference Paper
| Authors: | Hu, Y. C.; Grossberg, M. D.; Mageras, G. S. |
| Title: | Fast graph-based medical image segmentation with expert guided statistical information |
| Conference Title: | 10th IEEE International Conference on Information Technology and Applications in Biomedicine (ITAB 2010) |
| Abstract: | In radiotherapy treatment planning, delineation of normal organs at risk in images is one of the most timeconsuming tasks carried out routinely by human experts. Previously we proposed a speedy semi-automatic segmentation method based on a statistical graphical model, Conditional Random F ield (CRF,) from which an energy function is defined to obtain Maximum-a-posteriori (MAP) estimation of the segmentation via a fast graph cut algorithm. The probabilistic regional and boundary terms in the energy function are estimated from the training samples collected locally from the the human expert via interactive tool or a training database. In this paper, we present a simple acceleration technique that dramatically improves the speed without sacrificing the accuracy of the segmentation. In the context of slice-by-slice medical image segmentation, we accelerate the process by partially reusing the graph constructed from a previous segmented slice based on the likeness of two consecutive images. Experiment results in 5 liver cases show differences between the manually segmented volumes and our estimated volumes were less than 5%. The differences are within the normal variation of manual segmentation from inter - and intra-observers. Accelerated segmentations show no degradation in terms of accuracy compared to full segmentations. The computation time per slice is within 300 millisecond CPU time for full segmentation and 110 millisecond for accelerated segmentation. The semi-automatic segmentation method proposed achieves similar segmentation done by human expert in significantly lesser time while preserving the human oversight required during the treatment planning process. © 2010 IEEE. |
| Keywords: | treatment planning; medical imaging; information technology; training sample; image segmentation; acceleration technique; boundary term; computation time; consecutive images; cpu time; energy functions; graph cut; graph-based; graphical model; human expert; human oversight; interactive tool; manual segmentation; maximum a posteriori; medical image segmentation; organs at risks; radiotherapy treatment planning; semi-automatic segmentation; statistical information; time-consuming tasks; training database; graphic methods |
| Journal Title | Proceedings of the IEEE International Conference on Information Technology Applications in Biomedicine, ITAB |
| Volume: | 2008 |
| Conference Dates: | 2010 Nov 2-5 |
| Conference Location: | Corfu, Greece |
| ISBN: | 978-1-4244-6559-0 |
| Publisher: | IEEE |
| Date Published: | 2010-01-01 |
| Start Page: | 3099 |
| End Page: | 3102 |
| Language: | English |
| DOI: | 10.1109/ITAB.2010.5687812 |
| PROVIDER: | scopus |
| DOI/URL: | |
| Notes: | --- - Proceedings of the IEEE/EMBS Region 8 International Conference on Information Technology Applications in Biomedicine, ITAB - Proc IEEE EMBS Reg 8 Int Conf Inf Technol Appl Biomed ITAB - "Conference code: 83786" - "Export Date: 20 April 2011" - "Article No.: 5687812" - "Sponsors: University of Ioannina; National Technical University of Athens; University of Patras; Ionian University; Unit Med. Technol. Intelligent Inf. Syst., Univ. Ioannina" - 2 November 2010 through 5 November 2010 - "Source: Scopus" |
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