标题： MedRegion-CT：面向区域的多模态大语言模型用于全面的3D CT报告生成 显示英文标题

标题： MedRegion-CT: Region-Focused Multimodal LLM for Comprehensive 3D CT Report Generation

摘要： 最近发布的RadGenome-Chest CT显著推进了基于CT的报告生成。 然而，现有方法主要关注全局特征，难以捕捉区域特定细节，这可能导致某些异常被忽视。 为了解决这个问题，我们提出了 MedRegion-CT，一种以区域为中心的多模态大语言模型（MLLM）框架，具有三个关键创新。 首先，我们引入了区域 代表性（$R^2$）标记池化，该方法利用一个二维预训练视觉模型来高效提取三维CT特征。 这种方法生成代表整体切片特征的全局标记和突出目标区域的区域标记，使MLLM能够有效处理全面信息。 其次，一个通用分割模型生成伪掩码，然后由掩码编码器处理以提取以区域为中心的特征。 这使得MLLM能够专注于临床相关的区域，使用六个预定义的区域掩码。 第三，我们利用分割结果提取患者特定的属性，包括器官大小、直径和位置。 这些被转换成文本提示，丰富了 MLLM对患者特定背景的理解。 为了确保严格的评估，我们在RadGenome-Chest CT上进行了报告生成的基准实验。MedRegion-CT达到了最先进的性能，在自然语言生成质量和临床相关性方面优于现有方法，同时保持了可解释性。 我们框架的代码是公开的。 显示英文摘要

摘要： The recent release of RadGenome-Chest CT has significantly advanced CT-based report generation. However, existing methods primarily focus on global features, making it challenging to capture region-specific details, which may cause certain abnormalities to go unnoticed. To address this, we propose MedRegion-CT, a region-focused Multi-Modal Large Language Model (MLLM) framework, featuring three key innovations. First, we introduce Region Representative ($R^2$) Token Pooling, which utilizes a 2D-wise pretrained vision model to efficiently extract 3D CT features. This approach generates global tokens representing overall slice features and region tokens highlighting target areas, enabling the MLLM to process comprehensive information effectively. Second, a universal segmentation model generates pseudo-masks, which are then processed by a mask encoder to extract region-centric features. This allows the MLLM to focus on clinically relevant regions, using six predefined region masks. Third, we leverage segmentation results to extract patient-specific attributions, including organ size, diameter, and locations. These are converted into text prompts, enriching the MLLM's understanding of patient-specific contexts. To ensure rigorous evaluation, we conducted benchmark experiments on report generation using the RadGenome-Chest CT. MedRegion-CT achieved state-of-the-art performance, outperforming existing methods in natural language generation quality and clinical relevance while maintaining interpretability. The code for our framework is publicly available.