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Irving, B.* ; Cifor, A.* ; Papiez, B.W.* ; Franklin, J.* ; Anderson, E.M.* ; Brady, S.M.* ; Schnabel, J.A.*

Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics.

In: (International Conference on Medical Image Computing and Computer-Assisted Intervention). Berlin [u.a.]: Springer, 2014. 609-616 (Lect. Notes Comput. Sc. ; 8673 LNCS)
DOI PMC
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a powerful protocol for assessing tumour progression from changes in tissue contrast enhancement. Manual colorectal tumour delineation is a challenging and time consuming task due to the complex enhancement patterns in the 4D sequence. There is a need for a consistent approach to colorectal tumour segmentation in DCE-MRI and we propose a novel method based on detection of the tumour from signal enhancement characteristics of homogeneous tumour subregions and their neighbourhoods. Our method successfully detected 20 of 23 cases with a mean Dice score of 0.68 ± 0.15 compared to expert annotations, which is not significantly different from expert inter-rater variability of 0.73 ± 0.13 and 0.77 ± 0.10. In comparison, a standard DCE-MRI tumour segmentation technique, fuzzy c-means, obtained a Dice score of 0.28 ± 0.17.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Conference on Medical Image Computing and Computer-Assisted Intervention
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 8673 LNCS, Heft: PART 1, Seiten: 609-616 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)