Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images
Posted on 2019-10-27 - 06:37
Abstract Background Liver alignment between series/exams is challenged by dynamic morphology or variability in patient positioning or motion. Image registration can improve image interpretation and lesion co-localization. We assessed the performance of a convolutional neural network algorithm to register cross-sectional liver imaging series and compared its performance to manual image registration. Methods Three hundred fourteen patients, including internal and external datasets, who underwent gadoxetate disodium-enhanced magnetic resonance imaging for clinical care from 2011 to 2018, were retrospectively selected. Automated registration was applied to all 2,663 within-patient series pairs derived from these datasets. Additionally, 100 within-patient series pairs from the internal dataset were independently manually registered by expert readers. Liver overlap, image correlation, and intra-observation distances for manual versus automated registrations were compared using paired t tests. Influence of patient demographics, imaging characteristics, and liver uptake function was evaluated using univariate and multivariate mixed models. Results Compared to the manual, automated registration produced significantly lower intra-observation distance (p < 0.001) and higher liver overlap and image correlation (p < 0.001). Intra-exam automated registration achieved 0.88 mean liver overlap and 0.44 mean image correlation for the internal dataset and 0.91 and 0.41, respectively, for the external dataset. For inter-exam registration, mean overlap was 0.81 and image correlation 0.41. Older age, female sex, greater inter-series time interval, differing uptake, and greater voxel size differences independently reduced automated registration performance (p ⤠0.020). Conclusion A fully automated algorithm accurately registered the liver within and between examinations, yielding better liver and focal observation co-localization compared to manual registration.
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Hasenstab, Kyle; Cunha, Guilherme; Higaki, Atsushi; Ichikawa, Shintaro; Wang, Kang; Delgado, Timo; et al. (2019). Fully automated convolutional neural network-based affine algorithm improves liver registration and lesion co-localization on hepatobiliary phase T1-weighted MR images. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.4714838.v1
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AUTHORS (13)
KH
Kyle Hasenstab
GC
Guilherme Cunha
AH
Atsushi Higaki
SI
Shintaro Ichikawa
KW
Kang Wang
TD
Timo Delgado
RB
Ryan Brunsing
AS
Alexandra Schlein
LB
Leornado Bittencourt
AS
Armin Schwartzman
KF
Katie Fowler
AH
Albert Hsiao
CS
Claude Sirlin