标题： RAM-W600：类风湿性关节炎的多任务手腕数据集和基准 显示英文标题

标题： RAM-W600: A Multi-Task Wrist Dataset and Benchmark for Rheumatoid Arthritis

摘要： 类风湿性关节炎（RA）是一种常见的自身免疫性疾病，一直是计算机辅助诊断（CAD）和疾病监测研究的重点。 在临床环境中，由于成本低且易于获取，常规X线摄影（CR）被广泛用于RA的筛查和评估。 手腕是RA诊断的关键区域。 然而，该领域的CAD研究仍然有限，主要是由于获取高质量实例级标注的挑战。 (i) 手腕包含许多小骨头，关节间隙狭窄，结构复杂且经常重叠，需要详细的解剖知识才能进行准确标注。 (ii) RA的疾病进展通常会导致骨赘、骨侵蚀（BE）甚至骨性强直，这些变化会改变骨骼形态并增加标注难度，需要风湿病学的专业知识。 本工作提出了一个针对CR手腕骨骼的多任务数据集，包括两个任务：(i) 手腕骨骼实例分割和(ii) Sharp/ van der Heijde（SvdH）BE评分，这是首个公开的手腕骨骼实例分割资源。 该数据集包含来自四个医疗中心的227名患者的621张手腕常规X线片，其中443张图像具有像素级实例分割标注，548张图像具有SvdH BE评分。 该数据集可能支持与RA相关的广泛研究任务，包括关节间隙变窄（JSN）进展量化、BE检测、骨骼畸形评估和骨赘检测。 它还可以应用于其他与手腕相关的任务，如腕骨骨折定位。 我们希望这个数据集能显著降低手腕RA研究的门槛，并加速RA相关领域的CAD研究进展。 显示英文摘要

摘要： Rheumatoid arthritis (RA) is a common autoimmune disease that has been the focus of research in computer-aided diagnosis (CAD) and disease monitoring. In clinical settings, conventional radiography (CR) is widely used for the screening and evaluation of RA due to its low cost and accessibility. The wrist is a critical region for the diagnosis of RA. However, CAD research in this area remains limited, primarily due to the challenges in acquiring high-quality instance-level annotations. (i) The wrist comprises numerous small bones with narrow joint spaces, complex structures, and frequent overlaps, requiring detailed anatomical knowledge for accurate annotation. (ii) Disease progression in RA often leads to osteophyte, bone erosion (BE), and even bony ankylosis, which alter bone morphology and increase annotation difficulty, necessitating expertise in rheumatology. This work presents a multi-task dataset for wrist bone in CR, including two tasks: (i) wrist bone instance segmentation and (ii) Sharp/van der Heijde (SvdH) BE scoring, which is the first public resource for wrist bone instance segmentation. This dataset comprises 621 wrist conventional radiographs of 227 patients from four medical centers, with pixel-level instance segmentation annotations for 443 images and SvdH BE scores for 548 images. This dataset can potentially support a wide range of research tasks related to RA, including joint space narrowing (JSN) progression quantification, BE detection, bone deformity evaluation, and osteophyte detection. It may also be applied to other wrist-related tasks, such as carpal bone fracture localization. We hope this dataset will significantly lower the barrier to research on wrist RA and accelerate progress in CAD research within the RA-related domain.