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

• URL: http://arxiv.org/abs/2108.00831v1
• Date: Mon, 2 Aug 2021 12:41:58 GMT
• Title: Projective Skip-Connections for Segmentation Along a Subset of Dimensions in Retinal OCT
• Authors: Dmitrii Lachinov, Philipp Seeboeck, Julia Mai, Ursula Schmidt-Erfurth, Hrvoje Bogunovic
• Abstract summary: In medical imaging, there are clinically relevant segmentation tasks where the output mask is a projection to a subset of input image dimensions. We propose a novel convolutional neural network architecture that can effectively learn to produce a lower-dimensional segmentation mask. The network restores encoded representation only in a subset of input spatial dimensions and keeps the representation unchanged in the others.
• Score: 5.252775177165399
• License: http://creativecommons.org/licenses/by/4.0/
• 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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