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Clough, J.R.* ; Öksüz, I.* ; Byrne, N.* ; Schnabel, J.A.* ; King, A.P.*

Explicit topological priors for deep-Learning based image segmentation using persistent homology.

In:. Berlin [u.a.]: Springer, 2019. 16-28 (Lect. Notes Comput. Sc. ; 11492 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so. Our method uses the concept of persistent homology, a tool from topological data analysis, to capture high-level topological characteristics of segmentation results in a way which is differentiable with respect to the pixelwise probability of being assigned to a given class. The topological prior knowledge consists of the sequence of desired Betti numbers of the segmentation. As a proof-of-concept we demonstrate our approach by applying it to the problem of left-ventricle segmentation of cardiac MR images of subjects from the UK Biobank dataset, where we show that it improves segmentation performance in terms of topological correctness without sacrificing pixelwise accuracy.
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Publikationstyp Artikel: Sammelbandbeitrag/Buchkapitel
Korrespondenzautor
Schlagwörter Cardiac Mri ; Persistent Homology ; Segmentation ; Topological Data Analysis ; Topology
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11492 LNCS, Heft: , Seiten: 16-28 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)