PDF(Pubmed)
Abstract：
UNASSIGNED: Interactive segmentation seeks to incorporate human knowledge into segmentation models and thereby reducing the total amount of editing of auto-segmentations. By performing only interactions which provide new information, segmentation performance may increase cost-effectively. The aim of this study was to develop, evaluate and test feasibility of a deep learning-based single-cycle interactive segmentation model with the input being computer tomography (CT) and a small amount of information rich contours.
UNASSIGNED: A single-cycle interactive segmentation model, which took CT and the most cranial and caudal contour slices for each of 16 organs-at-risk for head-and-neck cancer as input, was developed. A CT-only model served as control. The models were evaluated with Dice similarity coefficient, Hausdorff Distance 95th percentile and average symmetric surface distance. A subset of 8 organs-at-risk were selected for a feasibility test. In this, a designated radiation oncologist used both single-cycle interactive segmentation and atlas-based auto-contouring for three cases. Contouring time and added path length were recorded.
UNASSIGNED: The medians of Dice coefficients increased with single-cycle interactive segmentation in the range of 0.004 (Brain)-0.90 (EyeBack_merged) when compared to CT-only. In the feasibility test, contouring time and added path length were reduced for all three cases as compared to editing atlas-based auto-segmentations.
UNASSIGNED: Single-cycle interactive segmentation improved segmentation metrics when compared to the CT-only model and was clinically feasible from a technical and usability point of view. The study suggests that it may be cost-effective to add a small amount of contouring input to deep learning-based segmentation models.
UNASSIGNED: A single-cycle interactive segmentation model, which took CT and the most cranial and caudal contour slices for each of 16 organs-at-risk for head-and-neck cancer as input, was developed. A CT-only model served as control. The models were evaluated with Dice similarity coefficient, Hausdorff Distance 95th percentile and average symmetric surface distance. A subset of 8 organs-at-risk were selected for a feasibility test. In this, a designated radiation oncologist used both single-cycle interactive segmentation and atlas-based auto-contouring for three cases. Contouring time and added path length were recorded.
UNASSIGNED: The medians of Dice coefficients increased with single-cycle interactive segmentation in the range of 0.004 (Brain)-0.90 (EyeBack_merged) when compared to CT-only. In the feasibility test, contouring time and added path length were reduced for all three cases as compared to editing atlas-based auto-segmentations.
UNASSIGNED: Single-cycle interactive segmentation improved segmentation metrics when compared to the CT-only model and was clinically feasible from a technical and usability point of view. The study suggests that it may be cost-effective to add a small amount of contouring input to deep learning-based segmentation models.
摘要：
交互式分割试图将人类知识结合到分割模型中,从而减少自动分割的编辑总量。通过仅执行提供新信息的交互，分割性能可以提高成本效益。这项研究的目的是发展，评估和测试基于深度学习的单周期交互式分割模型的可行性，输入为计算机断层扫描（CT）和少量信息丰富的轮廓。
■单周期交互式分割模型，输入16个头颈部癌症高危器官的CT和最头部和尾部轮廓切片，已开发。仅CT模型用作对照。用Dice相似系数对模型进行评价，Hausdorff距离第95百分位数和平均对称表面距离。选择8个危险器官的子集进行可行性测试。在此,指定的放射肿瘤学家对3例病例同时使用了单周期交互式分割和基于图谱的自动轮廓绘制.记录轮廓时间和增加的路径长度。
■与仅CT相比，Dice系数的中位数随着单周期交互式分割在0.004（Brain）-0.90（EyeBack_maled）范围内增加。在可行性测试中，与编辑基于图集的自动分割相比，这三种情况下的轮廓时间和增加的路径长度都减少了。
■与仅CT模型相比，单周期交互式分割改善了分割指标，并且从技术和可用性的角度来看在临床上是可行的。该研究表明，将少量的轮廓输入添加到基于深度学习的分割模型可能具有成本效益。
■单周期交互式分割模型，输入16个头颈部癌症高危器官的CT和最头部和尾部轮廓切片，已开发。仅CT模型用作对照。用Dice相似系数对模型进行评价，Hausdorff距离第95百分位数和平均对称表面距离。选择8个危险器官的子集进行可行性测试。在此,指定的放射肿瘤学家对3例病例同时使用了单周期交互式分割和基于图谱的自动轮廓绘制.记录轮廓时间和增加的路径长度。
■与仅CT相比，Dice系数的中位数随着单周期交互式分割在0.004（Brain）-0.90（EyeBack_maled）范围内增加。在可行性测试中，与编辑基于图集的自动分割相比，这三种情况下的轮廓时间和增加的路径长度都减少了。
■与仅CT模型相比，单周期交互式分割改善了分割指标，并且从技术和可用性的角度来看在临床上是可行的。该研究表明，将少量的轮廓输入添加到基于深度学习的分割模型可能具有成本效益。
