Semi-supervised, Topology-Aware Segmentation of Tubular Structures from Live Imaging 3D Microscopy

• URL: http://arxiv.org/abs/2105.09737v1
• Date: Thu, 20 May 2021 13:35:44 GMT
• Title: Semi-supervised, Topology-Aware Segmentation of Tubular Structures from Live Imaging 3D Microscopy
• Authors: Kasra Arnavaz, Oswin Krause, Jelena M. Krivokapic, Silja Heilmann, Jakob Andreas B{\ae}rentzen, Pia Nyeng, Aasa Feragen
• Abstract summary: This paper tackles two commonly encountered problems in biomedical imaging: Topological consistency of the segmentation, and limited annotations. We propose a topological score which measures both topological and geometric consistency between the predicted and ground truth segmentations. Our contributions are validated on a challenging segmentation task, locating tubular structures in the fetal pancreas from noisy live imaging confocal microscopy.
• Score: 6.2651370198971295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by a challenging tubular network segmentation task, this paper tackles two commonly encountered problems in biomedical imaging: Topological consistency of the segmentation, and limited annotations. We propose a topological score which measures both topological and geometric consistency between the predicted and ground truth segmentations, applied for model selection and validation. We apply our topological score in three scenarios: i. a U-net ii. a U-net pretrained on an autoencoder, and iii. a semisupervised U-net architecture, which offers a straightforward approach to jointly training the network both as an autoencoder and a segmentation algorithm. This allows us to utilize un-annotated data for training a representation that generalizes across test data variability, in spite of our annotated training data having very limited variation. Our contributions are validated on a challenging segmentation task, locating tubular structures in the fetal pancreas from noisy live imaging confocal microscopy.

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