Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy

Kasra Arnavaz1, Oswin Krause1, Kilian Zepf2, Jakob Andreas Bærentzen2, Jelena M. Krivokapic1, Silja Heilmann1, Pia Nyeng3, Aasa Feragen2
1: University of Copenhagen, Denmark, 2: Tecnhical University of Denmark, Lyngby, 3: Roskilde University College, Denmark
June 2022 issue
Publication date: 2022/07/05
PDF · arXiv


Motivated by the challenging segmentation task of pancreatic tubular networks, this paper tackles two commonly encountered problems in biomedical imaging: Topological consistency of the segmentation, and expensive or difficult annotation. Our contributions are the following: a) We propose a topological score which measures both topological and geometric consistency between the predicted and ground truth segmentations, applied to model selection and validation. b) We provide a full deep-learning methodology for this difficult noisy task on time-series image data. In our method, we first use a semisupervised U-net architecture, applicable to generic segmentation tasks, which jointly trains an autoencoder and a segmentation network. We then use tracking of loops over time to further improve the predicted topology. This semi-supervised approach allows us to utilize unannotated data to learn feature representations that generalize to test data with high 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. We show that our semi-supervised model outperforms not only fully supervised and pre-trained models but also an approach which takes topological consistency into account during training. Further, our approach achieves a mean loop score of 0.808 for detecting loops in the fetal pancreas, compared to a U-net trained with clDice with mean loop score 0.762.


topology · semisupervised · segmentation · tubular · confocal microscopy · loop tracking

Bibtex @article{melba:2022:015:arnavaz, title = "Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy", author = "Arnavaz, Kasra and Krause, Oswin and Zepf, Kilian and Bærentzen, Jakob Andreas and Krivokapic, Jelena M. and Heilmann, Silja and Nyeng, Pia and Feragen, Aasa", journal = "Machine Learning for Biomedical Imaging", volume = "1", issue = "June 2022 issue", year = "2022", issn = "2766-905X", url = "https://melba-journal.org/papers/2022:015.html" }
RISTY - JOUR AU - Arnavaz, Kasra AU - Krause, Oswin AU - Zepf, Kilian AU - Bærentzen, Jakob Andreas AU - Krivokapic, Jelena M. AU - Heilmann, Silja AU - Nyeng, Pia AU - Feragen, Aasa PY - 2022 TI - Quantifying Topology In Pancreatic Tubular Networks From Live Imaging 3D Microscopy T2 - Machine Learning for Biomedical Imaging VL - 1 IS - June 2022 issue SN - 2766-905X UR - https://www.melba-journal.org/papers/2022:015.html ER -

2022:015 cover