Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning

TL;DR

This paper presents a transfer learning-based method using a modified MultiPlanar UNet for accurate hip joint segmentation from CT scans, reducing manual editing and improving geometric fidelity for finite element modeling.

Contribution

It introduces a novel transfer learning strategy combined with interactive fine-tuning and post-processing to enhance segmentation accuracy of fine features in hip joints.

Findings

Achieved clinically validated segmentation accuracy on public CT datasets.

Reduced manual editing time for finite element model preparation.

Demonstrated robustness of the approach across different datasets.

Abstract

Accurate geometry representation is essential in developing finite element models. Although generally good, deep-learning segmentation approaches with only few data have difficulties in accurately segmenting fine features, e.g., gaps and thin structures. Subsequently, segmented geometries need labor-intensive manual modifications to reach a quality where they can be used for simulation purposes. We propose a strategy that uses transfer learning to reuse datasets with poor segmentation combined with an interactive learning step where fine-tuning of the data results in anatomically accurate segmentations suitable for simulations. We use a modified MultiPlanar UNet that is pre-trained using inferior hip joint segmentation combined with a dedicated loss function to learn the gap regions and post-processing to correct tiny inaccuracies on symmetric classes due to rotational invariance. We demonstrate this robust yet conceptually simple approach applied with clinically validated results on publicly available computed tomography scans of hip joints. Code and resulting 3D models are available at: https://github.com/MICCAI2022-155/AuToSeg}