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Kerfoot, E.* ; Clough, J.* ; Öksüz, I.* ; Lee, J.* ; King, A.P.* ; Schnabel, J.A.*

Left-ventricle quantification using residual U-net.

In: (International Workshop on Statistical Atlases and Computational Models of the Heart). Berlin [u.a.]: Springer, 2019. 371-380 (Lect. Notes Comput. Sc. ; 11395 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Estimating dimensional measurements of the left ventricle provides diagnostic values which can be used to assess cardiac health and identify certain pathologies. In this paper we describe our methodology of calculating measurements from left ventricle segmentations automatically generated using deep learning. We use a U-net convolutional neural network architecture built from residual units to segment the left ventricle and then process these segmentations to estimate the area of the cavity and myocardium, the dimensions of the cavity, and the thickness of the myocardium. Determining if an image is part of the diastolic or systolic portion of the cardiac cycle is done by analysing the cavity volume. The quality of our results are dependent on our training regime where we have generated a large derivative dataset by augmenting the original images with free-form deformations. Our expanded training set, in conjunction with simple affine image transforms, creates a sufficiently large training population to prevent over-fitting of the network while still creating an accurate and robust segmentation network. Assessing our method on the STACOM18 LVQuan challenge dataset we find that it significantly outperforms the previously published state-of-the-art on a 5-fold validation all tasks considered.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Cardiac Mr ; Cardiac Quantification ; Convolutional Neural Networks
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel International Workshop on Statistical Atlases and Computational Models of the Heart
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 11395 LNCS, Heft: , Seiten: 371-380 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)