用少量涂鸦标注实现高精度细胞分割，突破标注成本瓶颈。
ImPartial: Multi-channel Whole-Cell Segmentation using Partial Annotations
- 通过自监督多通道补全增强分割目标，减少对密集标注依赖。
- 在仅需部分标注下，性能媲美全监督模型，提升显著。
- 适用于多通道生物成像与临床病理图像，适合资源有限的研究者。
病理图像中的精确细胞分割通常需要密集的像素级标注，但此类标注成本高昂且耗时。这一挑战在新兴生物成像模态和多通道配置的数据集中尤为突出，因专家标注数据稀缺。本文提出ImPartial，一种深度学习框架，在低标注环境下利用稀疏涂鸦和有限监督实现顶尖的分割性能。该方法通过自监督多通道量化补全增强分割目标，基于观察：无需完美重建或去噪图像即可实现准确分割，因此引入更契合分割任务的自监督分类目标。实验表明，ImPartial在仅需少量标注的情况下，性能可与全监督模型相当。在多个基准多通道细胞成像及单通道临床明场免疫组化数据集上，均显著优于强基线模型。所有基准数据集与代码已开源：https://github.com/nadeemlab/ImPartial。
原文摘要 · Abstract (English)
Accurate cell segmentation in pathology images typically requires dense pixel-wise annotations, which are costly and time-consuming to obtain. This challenge is especially important for emerging biological imaging modalities and multiplexed datasets with variable channel configurations, where expert-labeled data are scarce. In this work, we introduce ImPartial, a deep learning framework designed to achieve state-of-the-art segmentation performance in low-annotation regimes using sparse scribbles and limited supervision. ImPartial augments the segmentation objective via self-supervised multi-channel quantized imputation. This approach leverages the observation that perfect pixel-wise reconstruction or denoising of the image is not needed for accurate segmentation, and thus, introduces a self-supervised classification objective that better aligns with the overall segmentation goal. We demonstrate that ImPartial achieves performance at par with fully supervised models while requiring substantially fewer annotations. Extensive experiments on benchmark multiplexed cellular imaging and single-plex clinical brightfield immunohistochemistry datasets show consistent improvements over strong baselines with only partial annotations. All benchmark datasets and code are available via our Github: https://github.com/nadeemlab/ImPartial.
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