Abstract：
BACKGROUND: The different tumor appearance of head and neck cancer across imaging modalities, scanners, and acquisition parameters accounts for the highly subjective nature of the manual tumor segmentation task. The variability of the manual contours is one of the causes of the lack of generalizability and the suboptimal performance of deep learning (DL) based tumor auto-segmentation models. Therefore, a DL-based method was developed that outputs predicted tumor probabilities for each PET-CT voxel in the form of a probability map instead of one fixed contour. The aim of this study was to show that DL-generated probability maps for tumor segmentation are clinically relevant, intuitive, and a more suitable solution to assist radiation oncologists in gross tumor volume segmentation on PET-CT images of head and neck cancer patients.
METHODS: A graphical user interface (GUI) was designed, and a prototype was developed to allow the user to interact with tumor probability maps. Furthermore, a user study was conducted where nine experts in tumor delineation interacted with the interface prototype and its functionality. The participants\' experience was assessed qualitatively and quantitatively.
RESULTS: The interviews with radiation oncologists revealed their preference for using a rainbow colormap to visualize tumor probability maps during contouring, which they found intuitive. They also appreciated the slider feature, which facilitated interaction by allowing the selection of threshold values to create single contours for editing and use as a starting point. Feedback on the prototype highlighted its excellent usability and positive integration into clinical workflows.
CONCLUSIONS: This study shows that DL-generated tumor probability maps are explainable, transparent, intuitive and a better alternative to the single output of tumor segmentation models.
METHODS: A graphical user interface (GUI) was designed, and a prototype was developed to allow the user to interact with tumor probability maps. Furthermore, a user study was conducted where nine experts in tumor delineation interacted with the interface prototype and its functionality. The participants\' experience was assessed qualitatively and quantitatively.
RESULTS: The interviews with radiation oncologists revealed their preference for using a rainbow colormap to visualize tumor probability maps during contouring, which they found intuitive. They also appreciated the slider feature, which facilitated interaction by allowing the selection of threshold values to create single contours for editing and use as a starting point. Feedback on the prototype highlighted its excellent usability and positive integration into clinical workflows.
CONCLUSIONS: This study shows that DL-generated tumor probability maps are explainable, transparent, intuitive and a better alternative to the single output of tumor segmentation models.
摘要：
背景：头颈部癌的不同影像学表现，扫描仪,和采集参数说明了手动肿瘤分割任务的高度主观性。手动轮廓的可变性是缺乏可泛化性和基于深度学习（DL）的肿瘤自动分割模型的次优性能的原因之一。因此,开发了一种基于DL的方法，该方法以概率图而不是一个固定轮廓的形式输出每个PET-CT体素的预测肿瘤概率。这项研究的目的是证明DL生成的肿瘤分割概率图具有临床相关性，直观，和更合适的解决方案来辅助放射肿瘤学家对头颈部癌症患者的PET-CT图像进行大体肿瘤体积分割。
方法：设计了图形用户界面（GUI），并开发了一个原型，允许用户与肿瘤概率图进行交互。此外，我们进行了一项用户研究,其中9位肿瘤勾画专家与界面原型及其功能进行了交互.对参与者的经验进行了定性和定量评估。
结果：对放射肿瘤学家的访谈表明，他们倾向于在轮廓绘制过程中使用彩虹色图来可视化肿瘤概率图，他们发现直觉。他们还赞赏滑块功能，它通过允许选择阈值来创建用于编辑和用作起点的单个轮廓来促进交互。对原型的反馈强调了其出色的可用性和与临床工作流程的积极整合。
结论：这项研究表明，DL生成的肿瘤概率图是可以解释的，透明,直观和更好的替代肿瘤分割模型的单一输出。
方法：设计了图形用户界面（GUI），并开发了一个原型，允许用户与肿瘤概率图进行交互。此外，我们进行了一项用户研究,其中9位肿瘤勾画专家与界面原型及其功能进行了交互.对参与者的经验进行了定性和定量评估。
结果：对放射肿瘤学家的访谈表明，他们倾向于在轮廓绘制过程中使用彩虹色图来可视化肿瘤概率图，他们发现直觉。他们还赞赏滑块功能，它通过允许选择阈值来创建用于编辑和用作起点的单个轮廓来促进交互。对原型的反馈强调了其出色的可用性和与临床工作流程的积极整合。
结论：这项研究表明，DL生成的肿瘤概率图是可以解释的，透明,直观和更好的替代肿瘤分割模型的单一输出。
