协同分割组织与细胞核，提升病理图像理解精度。
Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation
- 利用互引导提示机制，让组织与细胞核分割相互增强。
- 在PUMA数据集上，语义、实例和全景分割均优于现有方法。
- 适合医学图像分析、肿瘤微环境研究的科研人员使用。
组织病理学图像分析对肿瘤微环境与细胞形态学研究至关重要，但需同时分割组织区域与细胞核实例，面临巨大挑战。现有研究多单独处理组织语义分割或细胞核实例分割，忽视二者内在关联，导致病理理解不充分。为此，本文提出Co-Seg框架，实现组织与细胞核的协同分割。首先设计区域感知提示编码器（RP-Encoder），生成高质量的语义与实例区域提示作为先验约束；进而构建互引导提示掩码解码器（MP-Decoder），通过交叉引导增强任务间上下文一致性，协同计算语义与实例分割掩码。在PUMA数据集上的大量实验表明，所提Co-Seg在肿瘤组织与细胞核的语义、实例及全景分割任务中均超越当前最优方法。代码已开源：https://github.com/xq141839/Co-Seg。
原文摘要 · Abstract (English)
Histopathology image analysis is critical yet challenged by the demand of segmenting tissue regions and nuclei instances for tumor microenvironment and cellular morphology analysis. Existing studies focused on tissue semantic segmentation or nuclei instance segmentation separately, but ignored the inherent relationship between these two tasks, resulting in insufficient histopathology understanding. To address this issue, we propose a Co-Seg framework for collaborative tissue and nuclei segmentation. Specifically, we introduce a novel co-segmentation paradigm, allowing tissue and nuclei segmentation tasks to mutually enhance each other. To this end, we first devise a region-aware prompt encoder (RP-Encoder) to provide high-quality semantic and instance region prompts as prior constraints. Moreover, we design a mutual prompt mask decoder (MP-Decoder) that leverages cross-guidance to strengthen the contextual consistency of both tasks, collaboratively computing semantic and instance segmentation masks. Extensive experiments on the PUMA dataset demonstrate that the proposed Co-Seg surpasses state-of-the-arts in the semantic, instance and panoptic segmentation of tumor tissues and nuclei instances. The source code is available at https://github.com/xq141839/Co-Seg.
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