Gibson, E;
Hu, Y;
Ghavami, N;
Ahmed, HU;
Moore, C;
Emberton, M;
Huisman, HJ;
(2018)
Inter-site Variability in Prostate Segmentation Accuracy Using Deep Learning.
In: Frangi, A and Schnabel, J and Davatzikos, C and Alberola-López, C and Fichtinger, G, (eds.)
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science.
(pp. pp. 506-514).
Springer: Cham, Switzerland.
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Abstract
Deep-learning-based segmentation tools have yielded higher reported segmentation accuracies for many medical imaging applications. However, inter-site variability in image properties can challenge the translation of these tools to data from ‘unseen’ sites not included in the training data. This study quantifies the impact of inter-site variability on the accuracy of deep-learning-based segmentations of the prostate from magnetic resonance (MR) images, and evaluates two strategies for mitigating the reduced accuracy for data from unseen sites: training on multi-site data and training with limited additional data from the unseen site. Using 376 T2-weighted prostate MR images from six sites, we compare the segmentation accuracy (Dice score and boundary distance) of three deep-learning-based networks trained on data from a single site and on various configurations of data from multiple sites. We found that the segmentation accuracy of a single-site network was substantially worse on data from unseen sites than on data from the training site. Training on multi-site data yielded marginally improved accuracy and robustness. However, including as few as 8 subjects from the unseen site, e.g. during commissioning of a new clinical system, yielded substantial improvement (regaining 75% of the difference in Dice score).
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