发表机构
Shandong University（山东大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有分割方法缺乏语义引导导致肺部病灶边界勾画不佳的问题，提出概念引导分割模型CGSM，通过概念-视觉对齐模块和概念调制解码器整合大语言模型生成的概念，在QaTa-COV19数据集上取得91.59% Dice和84.49% mIoU的最优性能。
AI 中文摘要
肺部病灶的准确分割对于有效的临床诊断和治疗策略至关重要。现有的分割方法往往缺乏任务特定的语义引导，因为基于文本的标注通常仅提供病灶的粗略定位，导致病灶边界勾画不充分，且在小尺度病灶上表现不佳。为解决这一问题，我们提出了CGSM，一种概念引导分割模型，它将大语言模型生成并经临床审核的概念整合到分割过程中。具体而言，我们设计了一个概念-视觉对齐模块（CVAM），用于激活概念中与视觉特征对齐的相关令牌，增强文本与视觉信息之间的交互。此外，我们引入了一个概念调制解码器（CM-Decoder），该解码器利用来自CVAM的概念作为调制信号，促进图像与文本特征的适应性融合，从而提高分割精度。在两个公开数据集上进行的大量实验表明，CGSM达到了最先进的性能，在QaTa-COV19数据集上取得了91.59%的Dice系数和84.49%的mIoU结果，证明了其在肺部病灶分割中的有效性。
英文摘要
Accurate segmentation of pulmonary lesions is essential for effective clinical diagnosis and treatment strategies. Existing segmentation approaches often lack task-specific semantic guidance, as text-based annotations typically offer coarse localization of lesions, leading to inadequate delineation of lesion boundaries and poor performance on small-scale lesions. To address this, we propose CGSM, a Concept-Guided Segmentation Model that integrates LLM-generated and clinically reviewed concepts into the segmentation process. Specifically, we design a Concept-Visual Alignment Module (CVAM) to activate relevant tokens within the concepts that align with visual features, enhancing the interaction between textual and visual information. In addition, we introduce a Concept Modulated Decoder (CM-Decoder), which uses concepts from CVAM as modulation signals to facilitate the adaptive fusion of image and text features, improving the segmentation accuracy. Extensive experiments on two public datasets show that CGSM achieves state-of-the-art performance, with results of 91.59% Dice and 84.49% mIoU on the QaTa-COV19 dataset, demonstrating its effectiveness in pulmonary lesion segmentation.
