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Electrical Engineering and Systems Science > Image and Video Processing
Title: Auto-segmentation of Hip Joints using MultiPlanar UNet with Transfer learning
(Submitted on 17 Aug 2022 (v1), last revised 18 Aug 2022 (this version, v2))
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: this https URL}
Submission history
From: Peidi Xu [view email][v1] Wed, 17 Aug 2022 11:12:50 GMT (3792kb,D)
[v2] Thu, 18 Aug 2022 08:32:21 GMT (3792kb,D)
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