Abstract
Deterministic deep learning methods for image segmentation require very precise ground-truth labels. However, obtaining perfect segmentations for medical image analysis is highly time-consuming and usually not feasible. In ultrasound imaging this problem is especially pronounced, as ultrasound scans are challenged by low contrast, speckle and shadow artifacts, all contributing to imperfect manual labelling. To overcome the problem of imperfect labels, we propose a label correction step which can correct the imperfect ground-truth labels in the training set by applying active contours. This forces the ground-truth segmentations towards regions which coincide with edges in the original volume (and thus object boundaries). We demonstrated the proposed active contour correction with a standard U-Net on the boundary segmentation of the cavum septum pellucidum in 3D fetal brain ultrasound and on the segmentation of the left ventricle in 2D ultrasound scans. The active contour label correction yielded more precise boundary predictions, suggesting that this simple correction step can improve boundary segmentation with imperfect labels.
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Acknowledgements
L.S. Hesse acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Award. The authors are grateful for support from the Royal Academy of Engineering under the Engineering for Development Research Fellowship scheme.
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Hesse, L.S., Namburete, A.I.L. (2020). Improving U-Net Segmentation with Active Contour Based Label Correction. In: Papież, B., Namburete, A., Yaqub, M., Noble, J. (eds) Medical Image Understanding and Analysis. MIUA 2020. Communications in Computer and Information Science, vol 1248. Springer, Cham. https://doi.org/10.1007/978-3-030-52791-4_6
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