首个动态腹肌分割数据集，助力疝气研究与医学影像分割进步
DyABD: The Abdominal Muscle Segmentation in Dynamic MRI Benchmark
- 构建患者运动时采集的动态腹部MRI，引入极端解剖变化挑战分割
- 现有模型平均Dice系数仅0.82，表明医学图像分割仍有巨大提升空间
- 适合医学影像、计算机视觉及临床研究者关注，推动精准医疗发展
本文提出DyABD，首个针对腹疝患者动态腹部MRI的基准数据集，包含高质量腹肌标注。该数据集具有四大创新：(1) 首次定义腹部肌肉分割任务；(2) 患者执行不同动作时采集动态MRI，带来极端解剖变异，成为目前最具挑战性的分割数据集之一；(3) 包含术前与术后对比影像；(4) 推动腹疝高复发率的临床研究。本文还对现有分割模型在监督、少样本和零样本场景下的泛化能力进行了全面评估，结果显示多数方法在未见过的DyABD数据上仅达到0.82的平均Dice系数。这揭示了医学图像分割领域的真实进展，并重新定义了该领域的基准。
原文摘要 · Abstract (English)
This work introduces DyABD, a novel and complex benchmark dataset of dynamic abdominal MRIs from patients with abdominal hernias and associated high quality abdominal muscle annotations. DyABD is the first-of-its-kind in four key ways; (1) it proposes the first abdominal muscle segmentation task, (2) the dynamic MRIs are acquired whilst the patients perform various exercises, introducing extreme anatomical variability, making it one of the most challenging segmentation datasets to date, (3) it includes both pre and post corrective MRIs and (4) DyABD promotes clinical research into the high recurrence rates of abdominal hernias. Beyond dataset introduction, this work provides a comprehensive evaluation of the generalisation capabilities of existing segmentation models across Supervised, Few Shot and Zero Shot paradigms on the unseen DyABD dataset. This work reveals that there is still room for substantial improvement in the field of medical image segmentation, with the majority of techniques achieving a Dice Coefficient of 0.82. This work therefore sheds light on the true progress of the field and redefines the benchmark for progress in medical image segmentation.
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