Left-Ventricle Quantification Using Residual U-Net
Kerfoot E, Clough J, Oksuz I, Lee J, King AP, Schnabel JA (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Verlag
Book Volume: 11395 LNCS
Pages Range: 371-380
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Granada, ESP
ISBN: 9783030120283
DOI: 10.1007/978-3-030-12029-0_40
Abstract
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.
Involved external institutions
United Kingdom (GB)How to cite
APA:
Kerfoot, E., Clough, J., Oksuz, I., Lee, J., King, A.P., & Schnabel, J.A. (2019). Left-Ventricle Quantification Using Residual U-Net. In Alistair Young, Kawal Rhode, Mihaela Pop, Jichao Zhao, Kristin McLeod, Shuo Li, Maxime Sermesant, Tommaso Mansi (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 371-380). Granada, ESP: Springer Verlag.
MLA:
Kerfoot, Eric, et al. "Left-Ventricle Quantification Using Residual U-Net." Proceedings of the 9th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2018, held in conjunction with Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, ESP Ed. Alistair Young, Kawal Rhode, Mihaela Pop, Jichao Zhao, Kristin McLeod, Shuo Li, Maxime Sermesant, Tommaso Mansi, Springer Verlag, 2019. 371-380.
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