重新思考全身CT图像解读：一种以异常为中心的解析方法

摘要

CT图像的自动解读——尤其是在多平面和全身扫描中定位并描述异常发现——仍然是临床放射学领域的一项重大挑战。本研究旨在通过四项关键贡献应对这一挑战：（一）在分类体系方面，我们与资深放射科医生合作，提出了一套全面的层次化分类系统，涵盖所有身体区域的404种代表性异常发现；（二）在数据方面，我们贡献了一个包含超过14.5K张来自多平面及全身各区域的CT图像的数据集，并细致地为超过19K个异常提供了定位标注，每个异常均与详细描述相关联并纳入分类体系；（三）在模型开发上，我们提出了OminiAbnorm-CT，该模型能够基于文本查询自动在多平面和全身CT图像上定位并描述异常发现，同时支持通过视觉提示进行灵活交互；（四）在基准测试方面，我们基于真实临床场景建立了三项代表性评估任务。通过大量实验，我们证明OminiAbnorm-CT在所有任务和指标上均显著优于现有方法。

English

Automated interpretation of CT images-particularly localizing and describing abnormal findings across multi-plane and whole-body scans-remains a significant challenge in clinical radiology. This work aims to address this challenge through four key contributions: (i) On taxonomy, we collaborate with senior radiologists to propose a comprehensive hierarchical classification system, with 404 representative abnormal findings across all body regions; (ii) On data, we contribute a dataset containing over 14.5K CT images from multiple planes and all human body regions, and meticulously provide grounding annotations for over 19K abnormalities, each linked to the detailed description and cast into the taxonomy; (iii) On model development, we propose OminiAbnorm-CT, which can automatically ground and describe abnormal findings on multi-plane and whole-body CT images based on text queries, while also allowing flexible interaction through visual prompts; (iv) On benchmarks, we establish three representative evaluation tasks based on real clinical scenarios. Through extensive experiments, we show that OminiAbnorm-CT can significantly outperform existing methods on all the tasks and metrics.

重新思考全身CT图像解读：一种以异常为中心的解析方法

Rethinking Whole-Body CT Image Interpretation: An Abnormality-Centric Approach

摘要

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