Auto-segmentation of Hip Joints Using MultiPlanar UNet with Transfer Learning

Peidi Xu*, Faezeh Moshfeghifar, Torkan Gholamalizadeh, Michael Bachmann Nielsen, Kenny Erleben, Sune Darkner

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

1 Citation (Scopus)
18 Downloads (Pure)

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.

Original languageEnglish
Title of host publicationMedical Image Learning with Limited and Noisy Data : First International Workshop, MILLanD 2022 Held in Conjunction with MICCAI 2022 Singapore, September 22, 2022 Proceedings
EditorsGhada Zamzmi, Sameer Antani, Sivaramakrishnan Rajaraman, Zhiyun Xue, Ulas Bagci, Marius George Linguraru
Number of pages10
PublisherSpringer Science and Business Media Deutschland GmbH
Publication date2022
Pages153-162
ISBN (Print) 978-3-031-16759-1
ISBN (Electronic) 978-3-031-16760-7
DOIs
Publication statusPublished - 2022
Event1st International Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 - Singapore, Singapore
Duration: 22 Sep 202222 Sep 2022

Conference

Conference1st International Workshop on Medical Image Learning with Limited and Noisy Data, MILLanD 2022, held in conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022
Country/TerritorySingapore
CitySingapore
Period22/09/202222/09/2022
SeriesMedical Image Learning with Limited and Noisy Data
Volume13559
ISSN0302-9743

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • Finite element modeling
  • Segmentation
  • Transfer learning

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