Co-Seg：用于组织和细胞核分割的相互提示引导协作学习
Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation
AI总结：
针对组织病理学中组织和细胞核分割任务被分离处理的问题，提出Co-Seg框架，通过区域感知提示编码器和相互提示掩码解码器实现两任务相互增强，在PUMA数据集上达到最先进性能。
摘要：
组织病理学图像分析至关重要，但面临分割组织区域和细胞核实例以进行肿瘤微环境和细胞形态学分析的需求挑战。现有研究分别关注组织语义分割或细胞核实例分割，但忽略了这两个任务之间的内在关系，导致对组织病理学的理解不足。为解决这一问题，我们提出了Co-Seg框架，用于组织和细胞核的协作分割。具体而言，我们引入了一种新颖的共分割范式，使组织和细胞核分割任务能够相互增强。为此，我们首先设计了一个区域感知提示编码器（RP-Encoder），以提供高质量的语义和实例区域提示作为先验约束。此外，我们设计了一个相互提示掩码解码器（MP-Decoder），利用交叉引导来加强两个任务的上下文一致性，协作计算语义和实例分割掩码。在PUMA数据集上的大量实验表明，所提出的Co-Seg在肿瘤组织和细胞核实例的语义、实例和全景分割方面均超越了现有最先进方法。源代码可在https://github.com/xq141839/Co-Seg获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
