Alshammari Shorug S, Yaddanapudi Sridhar, Kušnik Blaž, Ivančič Rok, Anderle Kristjan, Li Jonathan G, Furutani Keith M, Beltran Chris J, Lu Bo
Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL, USA.
Department of Radiation Oncology, University of Florida, Gainesville, FL, USA.
Tech Innov Patient Support Radiat Oncol. 2024 Nov 26;32:100295. doi: 10.1016/j.tipsro.2024.100295. eCollection 2024 Dec.
We evaluated and benchmarked a novel deformable image registration (DIR) software functionality (DirOne, Cosylab d.d., Ljubljana, Slovenia) by comparing it to two commercial systems, MIM and VelocityAI, following AAPM task group 132 (TG-132) guidelines.
Three publicly available datasets were used for evaluation. The first dataset includes primary and deformed phantom images for a male pelvis. The second, from DIR-Lab, contains ten sets of 4D CT thoracic scans. The third dataset, from the DIR Evaluation Project (DIREP), includes ten head and neck CTs. VelocityAI and MIM served as benchmarks to assess DirOne's performance. Target registration error (TRE), dice similarity coefficient (DSC), and mean distance to agreement (MDA) were the evaluation metrics.
For TRE, the average results for DirOne, MIM, and VelocityAI were 3.3 ± 3.1 mm, 2.7 ± 3.7 mm, and 3.4 ± 2.4 mm, respectively. For DSC, DirOne achieved 0.96 ± 0.02, MIM 0.98 ± 0.02, and VelocityAI 0.98 ± 0.01 across the first and second datasets. In the DIREP dataset, DirOne achieved 0.73 ± 0.34 for MDA and 0.91 ± 0.03 for DSC; MIM achieved 0.54 ± 0.36 and 0.93 ± 0.02, and VelocityAI 0.93 ± 0.38 and 0.90 ± 0.03.
The novel DIR software demonstrated clinically acceptable accuracy compared to other commercial systems, supporting its potential use in radiotherapy treatment planning applications such as automatic image segmentation, 4D segmentation propagation, and dose warping.
我们按照美国医学物理师协会任务组132（TG - 132）指南，将一种新型可变形图像配准（DIR）软件功能（DirOne，Cosylab d.d.，卢布尔雅那，斯洛文尼亚）与两个商业系统MIM和VelocityAI进行比较，对其进行评估和基准测试。
使用三个公开可用的数据集进行评估。第一个数据集包括男性骨盆的原始和变形体模图像。第二个数据集来自DIR - Lab，包含十组4D CT胸部扫描。第三个数据集来自DIR评估项目（DIREP），包括十组头颈部CT。以VelocityAI和MIM作为基准来评估DirOne的性能。目标配准误差（TRE）、骰子相似系数（DSC）和平均一致距离（MDA）为评估指标。
对于TRE，DirOne、MIM和VelocityAI的平均结果分别为3.3±3.1毫米、2.7±3.7毫米和3.4±2.4毫米。对于DSC，在第一个和第二个数据集上，DirOne达到0.96±0.02，MIM达到0.98±0.02，VelocityAI达到0.98±0.01。在DIREP数据集中，DirOne的MDA为0.73±0.34，DSC为0.91±0.03；MIM的MDA为0.54±0.36，DSC为0.93±0.02；VelocityAI的MDA为0.93±0.38，DSC为0.90±0.03。
与其他商业系统相比，这种新型DIR软件显示出临床上可接受的准确性，支持其在放射治疗治疗计划应用中的潜在用途，如自动图像分割、4D分割传播和剂量变形。
