脑肿瘤分割是胶质母细胞瘤患者临床管理中不可或缺的一部分，胶质母细胞瘤是成人中最致命的原发性脑肿瘤。肿瘤的手动描绘非常耗时且高度依赖于提供者。必须通过引入基于深度学习的自动化分割工具来解决这两个问题。本研究旨在确定专家用于评估自动生成的分割质量及其纠正过程的思维过程的标准。使用多种方法来详细了解影响专家对分割质量及其思维过程的看法的复杂因素。纠正建议的分割。 2021 年 8 月至 12 月期间收集了来自神经肿瘤学家和神经放射学家的问卷调查和半结构化访谈的数据，并使用演绎和归纳相结合的方法进行分析。脑肿瘤是高度复杂且模糊的分割目标。因此，医生在评估质量和纠正脑肿瘤分割的需要时严重依赖与患者和临床背景相关的给定背景。最重要的是，预期的临床应用决定了分割质量标准和编辑决策。医生对人工智能算法功能的个人信念和偏好以及是否不应包括有问题的区域是影响分割质量感知和编辑分割外观的附加标准。我们对专家对分割质量感知的研究结果将允许设计改进了以专家为中心的脑肿瘤分割模型评估框架。特别是，这里介绍的知识可以激发用于分割模型训练和评估的脑肿瘤特定指标的开发。版权所有 © 2023。由 Elsevier Inc. 出版。

Brain tumor segmentations are integral to the clinical management of patients with glioblastoma, the deadliest primary brain tumor in adults. The manual delineation of tumors is time-consuming and highly provider-dependent. These two problems must be addressed by introducing automated, deep-learning-based segmentation tools. This study aimed to identify criteria experts use to evaluate the quality of automatically generated segmentations and their thought processes as they correct them.Multiple methods were used to develop a detailed understanding of the complex factors that shape experts' perception of segmentation quality and their thought processes in correcting proposed segmentations. Data from a questionnaire and semistructured interview with neuro-oncologists and neuroradiologists were collected between August and December 2021 and analyzed using a combined deductive and inductive approach.Brain tumors are highly complex and ambiguous segmentation targets. Therefore, physicians rely heavily on the given context related to the patient and clinical context in evaluating the quality and need to correct brain tumor segmentation. Most importantly, the intended clinical application determines the segmentation quality criteria and editing decisions. Physicians' personal beliefs and preferences about the capabilities of AI algorithms and whether questionable areas should not be included are additional criteria influencing the perception of segmentation quality and appearance of an edited segmentation.Our findings on experts' perceptions of segmentation quality will allow the design of improved frameworks for expert-centered evaluation of brain tumor segmentation models. In particular, the knowledge presented here can inspire the development of brain tumor-specific metrics for segmentation model training and evaluation.Copyright © 2023. Published by Elsevier Inc.