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

TL;DR

This paper introduces a semi-supervised deep learning approach with a topological score for segmenting pancreatic tubular networks in noisy 3D microscopy data, improving topological accuracy and generalization from limited annotations.

Contribution

It proposes a novel topological score and a semi-supervised U-net based methodology that enhances segmentation accuracy and topology consistency in challenging biomedical imaging tasks.

Findings

Semi-supervised model outperforms fully supervised models.

Achieved higher mean loop score (0.808) compared to clDice-based U-net (0.762).

Method effectively utilizes unannotated data for robust topology learning.

Abstract

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.