Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net

[论文] Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net

论文概要

研究领域: cs.CV, cs.AI 作者: Ricardo Coimbra Brioso, Lorenzo Mondo, Damiano Dei, Nicola Lambri, Pietro Mancosu, Marta Scorsetti, Daniele Loiacono 发布时间: 2026-04-13 arXiv: 2604.11798

中文摘要

临床靶区体积（CTV）的准确勾画对放疗计划至关重要，但耗时且难以评估，特别是对于全身骨髓和淋巴结照射（TMLI）等复杂治疗。虽然基于深度学习的自动分割可以减少工作量，但安全的临床部署需要可靠的线索来指示模型可能出错的位置。本文提出基于nnU-Net的预算感知不确定性驱动的质量保证框架，结合不确定性量化和事后校准生成体素级不确定性图。实验表明，结合校准的高效集成是实现预算感知QA工作流程的有前途的策略。

原文摘要

Accurate delineation of the Clinical Target Volume (CTV) is essential for radiotherapy planning, yet remains time-consuming and difficult to assess, especially for complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI). While deep learning-based auto-segmentation can reduce workload, safe clinical deployment requires reliable cues indicating where models may be wrong.

--- *自动采集于 2026-04-15*

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