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This paper presents our submission to the AortaSeg Challenge at MICCAI 2024, which focuses on multiclass segmentation of the aorta into anatomically defined zones and branches. We adopted a data-centric strategy, emphasizing rigorous data preparation and carefully designed pre- and post-processing steps over architectural novelty. Our approach is built upon a classical 3D RUNet-based model, demonstrating that such architectures remain highly competitive in low-data scenarios when paired with robust engineering practices. Specifically, we implemented a two-step, patch-based segmentation pipeline, incorporating targeted data augmentation and class-aware sampling during training. This design aimed to improve performance on small and underrepresented anatomical structures. On the official test dataset, our method achieved an average Dice Similarity Coefficient of 0.755 ± 0.038 and a Normalized Surface Distance of 0.788 ± 0.042, outperforming the baseline in the majority of evaluated regions. These results highlight the effectiveness of prioritizing data quality and processing techniques, and underscore the continued relevance of classical segmentation models in practical, data-constrained medical imaging tasks.

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Data-Centric Multiclass Aortic Segmentation: Revisiting Classical Architectures in Low-Data Regimes

  • 会议论文

  • 发表日期：

    2026年

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  • Marek Wodzinski,
  • Henning Müller

摘要

This paper presents our submission to the AortaSeg Challenge at MICCAI 2024, which focuses on multiclass segmentation of the aorta into anatomically defined zones and branches. We adopted a data-centric strategy, emphasizing rigorous data preparation and carefully designed pre- and post-processing steps over architectural novelty. Our approach is built upon a classical 3D RUNet-based model, demonstrating that such architectures remain highly competitive in low-data scenarios when paired with robust engineering practices. Specifically, we implemented a two-step, patch-based segmentation pipeline, incorporating targeted data augmentation and class-aware sampling during training. This design aimed to improve performance on small and underrepresented anatomical structures. On the official test dataset, our method achieved an average Dice Similarity Coefficient of 0.755 ± 0.038 and a Normalized Surface Distance of 0.788 ± 0.042, outperforming the baseline in the majority of evaluated regions. These results highlight the effectiveness of prioritizing data quality and processing techniques, and underscore the continued relevance of classical segmentation models in practical, data-constrained medical imaging tasks.