重新思考全身CT影像解讀：以異常為中心的策略

摘要

CT影像的自動解讀——尤其是在多平面及全身掃描中定位並描述異常發現——仍然是臨床放射學中的一項重大挑戰。本研究旨在通過四項關鍵貢獻來應對這一挑戰：（一）在分類學方面，我們與資深放射科醫師合作，提出了一套全面的層級分類系統，涵蓋所有身體區域的404種代表性異常發現；（二）在數據方面，我們貢獻了一個包含超過14.5K張來自多平面及所有人體區域CT影像的數據集，並精心提供了超過19K個異常的定位標註，每個異常均與詳細描述相連結並納入分類系統；（三）在模型開發方面，我們提出了OminiAbnorm-CT，該模型能夠基於文本查詢自動定位並描述多平面及全身CT影像上的異常發現，同時允許通過視覺提示進行靈活互動；（四）在基準測試方面，我們基於真實臨床場景建立了三項代表性評估任務。通過大量實驗，我們展示了OminiAbnorm-CT在所有任務和指標上均能顯著超越現有方法。

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影像解讀：以異常為中心的策略

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

摘要

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