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Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (MICCAI 2021)

Abstract

In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions. In this work, we propose a novel convolutional neural network architecture that can effectively learn to produce a lower-dimensional segmentation mask than the input image. The network restores encoded representation only in a subset of input spatial dimensions and keeps the representation unchanged in the others. The newly proposed projective skip-connections allow linking the encoder and decoder in a UNet-like structure. We evaluated the proposed method on two clinically relevant tasks in retinal Optical Coherence Tomography (OCT): geographic atrophy and retinal blood vessel segmentation. The proposed method outperformed the current state-of-the-art approaches on all the OCT datasets used, consisting of 3D volumes and corresponding 2D en-face masks. The proposed architecture fills the methodological gap between image classification and ND image segmentation.

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Acknowledgements

The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged.

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Correspondence to Dmitrii Lachinov .

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Lachinov, D., Seeböck, P., Mai, J., Goldbach, F., Schmidt-Erfurth, U., Bogunovic, H. (2021). Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12901. Springer, Cham. https://doi.org/10.1007/978-3-030-87193-2_41

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  • DOI: https://doi.org/10.1007/978-3-030-87193-2_41

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