Accurate segmentation of anatomical structures in Computed Tomography (CT) images is crucial for clinical diagnosis, treatment planning, and disease monitoring. The present deep learning segmentation methods are hindered by factors such as data scale and model size. Inspired by how doctors identify tissues, we propose a novel approach, the Prior Category Network (PCNet), that boosts segmentation performance by leveraging prior knowledge between different categories of anatomical structures. Our PCNet comprises three key components: prior category prompt (PCP), hierarchy category system (HCS), and hierarchy category loss (HCL). PCP utilizes Contrastive Language-Image Pretraining (CLIP), along with attention modules, to systematically define the relationships between anatomical categories as identified by clinicians. HCS guides the segmentation model in distinguishing between specific organs, anatomical structures, and functional systems through hierarchical relationships. HCL serves as a consistency constraint, fortifying the directional guidance provided by HCS to enhance the segmentation model’s accuracy and robustness. We conducted extensive experiments to validate the effectiveness of our approach, and the results indicate that PCNet can generate a high-performance, universal model for CT segmentation. The PCNet framework also demonstrates a significant transferability on multiple downstream tasks. The ablation experiments show that the methodology employed in constructing the HCS is of critical importance. The prompt and HCS can be accessed at https://github.com/PKU-MIPET/PCNet .

中文翻译：

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PCNet：CT 通用分割模型的先验类别网络


计算机断层扫描 （CT） 图像中解剖结构的准确分割对于临床诊断、治疗计划和疾病监测至关重要。目前的深度学习分割方法受到数据规模和模型大小等因素的阻碍。受医生如何识别组织的启发，我们提出了一种新方法，即先验类别网络 （PCNet），该方法通过利用不同类别解剖结构之间的先验知识来提高分割性能。我们的 PCNet 包括三个关键组成部分：先验类别提示 （PCP）、层次结构类别系统 （HCS） 和层次结构类别损失 （HCL）。PCP 利用对比语言-图像预训练 （CLIP） 以及注意力模块来系统定义临床医生确定的解剖类别之间的关系。HCS 指导分割模型通过分层关系区分特定器官、解剖结构和功能系统。HCL 充当一致性约束，加强 HCS 提供的方向指导，以提高分割模型的准确性和稳健性。我们进行了广泛的实验来验证我们方法的有效性，结果表明 PCNet 可以生成一个高性能的通用 CT 分割模型。PCNet 框架还展示了在多个下游任务上的重要可转移性。消融实验表明，构建 HCS 所采用的方法至关重要。提示和 HCS 可在 https://github.com/PKU-MIPET/PCNet 中访问。