重新审视3D医学涂鸦监督：超越心脏分割的基准测试
Revisiting 3D Medical Scribble Supervision: Benchmarking Beyond Cardiac Segmentation
AI总结：
针对涂鸦监督在医学3D分割中过度局限于心脏领域的问题，本文提出ScribbleBench基准，发现简单方法泛化优于复杂方法，并指出结合部分损失的nnU-Net是强大基线。
摘要：
涂鸦监督通过利用稀疏标注代替体素级标签，已成为降低医学3D分割标注成本的一种有前景的方法。尽管现有方法报告了强劲的性能，但深入分析表明，大多数研究局限于心脏领域，主要使用ACDC和MSCMR数据集。这种过度专业化导致了严重的过拟合、关于性能提升的误导性声明，以及在更广泛分割任务中缺乏泛化能力。在这项工作中，我们为实用的涂鸦监督制定了一套关键要求，并引入了ScribbleBench，这是一个涵盖七个以上多样化医学成像数据集的综合基准，用于系统评估这些要求的满足情况。因此，我们发现方法普遍无法在任务间泛化，许多广泛使用的新颖性方法在心脏领域之外会降低性能，而更简单被忽视的方法却实现了卓越的泛化。最后，我们呼吁关注一个强大却被忽视的基线，即结合了部分损失的nnU-Net，它在各种任务中始终优于专业方法。通过识别现有研究的基本局限并建立新的基准驱动评估标准，这项工作旨在引导涂鸦监督向更实用、鲁棒和可泛化的医学图像分割方法发展。
英文摘要：
Scribble supervision has emerged as a promising approach for reducing annotation costs in medical 3D segmentation by leveraging sparse annotations instead of voxel-wise labels. While existing methods report strong performance, a closer analysis reveals that the majority of research is confined to the cardiac domain, predominantly using ACDC and MSCMR datasets. This over-specialization has resulted in severe overfitting, misleading claims of performance improvements, and a lack of generalization across broader segmentation tasks. In this work, we formulate a set of key requirements for practical scribble supervision and introduce ScribbleBench, a comprehensive benchmark spanning over seven diverse medical imaging datasets, to systematically evaluate the fulfillment of these requirements. Consequently, we uncover a general failure of methods to generalize across tasks and that many widely used novelties degrade performance outside of the cardiac domain, whereas simpler overlooked approaches achieve superior generalization. Finally, we raise awareness for a strong yet overlooked baseline, nnU-Net coupled with a partial loss, which consistently outperforms specialized methods across a diverse range of tasks. By identifying fundamental limitations in existing research and establishing a new benchmark-driven evaluation standard, this work aims to steer scribble supervision toward more practical, robust, and generalizable methodologies for medical image segmentation.
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