Semi-automatic muscle segmentation in MR images using deep registration-based label propagation

•Registration-based label propagation is used for intra-subject muscle MR segmentation.•3D few-shot segmentation is reached by propagating 2D labels using deep registration.•Propagation is guided by image intensity, muscle shape and registration consistency.•Bidirectional propagation uses registrati...

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Vydáno v:Pattern recognition Ročník 140; číslo August 2023; s. 109529
Hlavní autoři: Decaux, Nathan, Conze, Pierre-Henri, Ropars, Juliette, He, Xinyan, Sheehan, Frances T., Pons, Christelle, Ben Salem, Douraied, Brochard, Sylvain, Rousseau, François
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Elsevier Ltd 01.08.2023
Elsevier
Témata:
Computer Science
Computer Vision and Pattern Recognition
Deep registration
Label propagation
Musculoskeletal system
Semi-automatic segmentation
Semi-automatic segmentation
Musculoskeletal system
Deep registration
Label propagation
deep registration
label propagation
musculoskeletal system
few-shot segmentation
semi-automatic segmentation
ISSN:0031-3203, 1873-5142
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Shrnutí:•Registration-based label propagation is used for intra-subject muscle MR segmentation.•3D few-shot segmentation is reached by propagating 2D labels using deep registration.•Propagation is guided by image intensity, muscle shape and registration consistency.•Bidirectional propagation uses registration quality estimation as weighting guidance.•An unsupervised pre-training stage initializes the deep registration framework. Fully automated approaches based on convolutional neural networks have shown promising performances on muscle segmentation from magnetic resonance (MR) images, but still rely on an extensive amount of training data to achieve valuable results. Muscle segmentation for pediatric and rare diseases cohorts is therefore still often done manually. Producing dense delineations over 3D volumes remains a time-consuming and tedious task, with significant redundancy between successive slices. In this work, we propose a segmentation method relying on registration-based label propagation, which provides 3D muscle delineations from a limited number of annotated 2D slices. Based on an unsupervised deep registration scheme, our approach ensures the preservation of anatomical structures by penalizing deformation compositions that do not produce consistent segmentation from one annotated slice to another. Evaluation is performed on MR data from lower leg and shoulder joints. Results demonstrate that the proposed semi-automatic multi-label segmentation model outperforms state-of-the-art techniques.
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ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2023.109529