标题： 从放射学报告中学习分割 显示英文标题

标题： Learning Segmentation from Radiology Reports

摘要： 肿瘤分割在CT扫描中对于诊断、手术和预后至关重要，但分割掩码稀缺，因为其创建需要时间和专业知识。 公共腹部CT数据集中的肿瘤掩码数量从几十到几千不等，但医院有数十万张肿瘤CT图像和放射科报告。 因此，利用报告来提高分割效果对于扩展至关重要。 在本文中，我们提出了一种报告监督损失（R-Super），将放射科报告转换为用于肿瘤分割的体素级监督。 我们创建了一个包含6,718对CT-报告的数据集（来自UCSF医院），并将其与公共CT-掩码数据集（来自AbdomenAtlas 2.0）合并。 我们使用R-Super结合这些掩码和报告进行训练，在内部和外部验证中显著提高了肿瘤分割效果——与仅使用掩码训练相比，F1分数最高提高了16%。 通过利用易于获得的放射科报告来补充稀缺的分割掩码，R-Super在仅有少量训练掩码（例如，50个）或大量掩码（例如，1.7K个）的情况下都显著提升了AI性能。 项目：https://github.com/MrGiovanni/R-Super 显示英文摘要

摘要： Tumor segmentation in CT scans is key for diagnosis, surgery, and prognosis, yet segmentation masks are scarce because their creation requires time and expertise. Public abdominal CT datasets have from dozens to a couple thousand tumor masks, but hospitals have hundreds of thousands of tumor CTs with radiology reports. Thus, leveraging reports to improve segmentation is key for scaling. In this paper, we propose a report-supervision loss (R-Super) that converts radiology reports into voxel-wise supervision for tumor segmentation AI. We created a dataset with 6,718 CT-Report pairs (from the UCSF Hospital), and merged it with public CT-Mask datasets (from AbdomenAtlas 2.0). We used our R-Super to train with these masks and reports, and strongly improved tumor segmentation in internal and external validation--F1 Score increased by up to 16% with respect to training with masks only. By leveraging readily available radiology reports to supplement scarce segmentation masks, R-Super strongly improves AI performance both when very few training masks are available (e.g., 50), and when many masks were available (e.g., 1.7K). Project: https://github.com/MrGiovanni/R-Super