Sparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms

TL;DR

This paper presents a semi-automatic training method using random walks for U-Net segmentation of biodegradable bone implants in microtomography images, reducing annotation effort while maintaining high accuracy.

Contribution

It introduces a novel random walk-based semi-automatic annotation approach that achieves dense supervision quality with sparse annotations, reducing manual labeling effort.

Findings

Random walk-based method matches dense supervision Dice scores.

Direct scribble training reduces segmentation quality significantly.

Proposed approach enables efficient training with minimal annotations.

Abstract

Currently, most bone implants used in orthopedics and traumatology are non-degradable and may need to be surgically removed later on e.g. in the case of children. This removal is associated with health risks which could be minimized by using biodegradable implants. Therefore, research on magnesium-based implants is ongoing, which can be objectively quantified through synchrotron radiation microtomography and subsequent image analysis. In order to evaluate the suitability of these materials, e.g. their stability over time, accurate pixelwise segmentations of these high-resolution scans are necessary. The fully-convolutional U-Net architecture achieves a Dice coefficient of 0.750 +/- 0.102 when trained with a small dataset with dense expert annotations. However, extending the learning to larger databases would require prohibitive annotation efforts. Hence, in this work we implemented and compared new training methods that require only a small fraction of manually annotated pixels. While directly training on these scribble annotation deteriorates the segmentation quality by 26.8 percentage points, our new random walk-based semi-automatic target achieves the same Dice overlap as a dense supervision, and thus offers a more promising approach for sparse annotations.