TY - GEN
T1 - Automated quality assessment of cardiac MR images using convolutional neural networks
AU - Zhang, Le
AU - Gooya, Ali
AU - Dong, Bo
AU - Hua, Rui
AU - Petersen, Steffen E.
AU - Medrano-Gracia, Pau
AU - Frangi, Alejandro F.
N1 - Publisher Copyright:
© Springer International Publishing AG 2016.
PY - 2016
Y1 - 2016
N2 - Image quality assessment (IQA) is crucial in large-scale population imaging so that high-throughput image analysis can extract meaningful imaging biomarkers at scale. Specifically, in this paper, we address a seemingly basic yet unmet need: the automatic detection of missing (apical and basal) slices in Cardiac Magnetic Resonance Imaging (CMRI) scans, which is currently performed by tedious visual assessment. We cast the problem as classification tasks, where the bottom and top slices are tested for the presence of typical basal and apical patterns. Inspired by the success of deep learning methods, we train Convolutional Neural Networks (CNN) to construct a set of discriminative features. We evaluated our approach on a subset of the UK Biobank datasets. Precision and Recall figures for detecting missing apical slice (MAS) (81.61% and 88.73 %) and missing basal slice (MBS) (74.10% and 88.75 %) are superior to other state-of-the-art deep learning architectures. Cross-dataset experiments show the generalization ability of our approach.
AB - Image quality assessment (IQA) is crucial in large-scale population imaging so that high-throughput image analysis can extract meaningful imaging biomarkers at scale. Specifically, in this paper, we address a seemingly basic yet unmet need: the automatic detection of missing (apical and basal) slices in Cardiac Magnetic Resonance Imaging (CMRI) scans, which is currently performed by tedious visual assessment. We cast the problem as classification tasks, where the bottom and top slices are tested for the presence of typical basal and apical patterns. Inspired by the success of deep learning methods, we train Convolutional Neural Networks (CNN) to construct a set of discriminative features. We evaluated our approach on a subset of the UK Biobank datasets. Precision and Recall figures for detecting missing apical slice (MAS) (81.61% and 88.73 %) and missing basal slice (MBS) (74.10% and 88.75 %) are superior to other state-of-the-art deep learning architectures. Cross-dataset experiments show the generalization ability of our approach.
UR - http://www.scopus.com/inward/record.url?scp=84994143505&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-46630-9_14
DO - 10.1007/978-3-319-46630-9_14
M3 - Conference contribution
AN - SCOPUS:84994143505
SN - 9783319466293
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 138
EP - 145
BT - Simulation and Synthesis in Medical Imaging
A2 - Tsaftaris, Sotirios A.
A2 - Gooya, Ali
A2 - Frangi, Alejandro F.
A2 - Prince, Jerry L.
PB - Springer-Verlag Italia
T2 - 1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Y2 - 21 October 2016 through 21 October 2016
ER -