METHODS: A segmentation pipeline was developed and evaluated using expert manual segmentations of MRI scans of 115 uterine fibroid patients, screened for and/or undergoing MR-HIFU treatment. The pipeline contained three separate neural networks, one per target structure. The first step in the pipeline was uterus segmentation from contrast-enhanced (CE)-T1w scans. This segmentation was subsequently used to remove non-uterus background tissue for NPV and fibroid segmentation. In the following step, NPVs were segmented from uterus-only CE-T1w scans. Finally, fibroids were segmented from uterus-only T2w scans. The segmentations were used to calculate the volume for each structure. Reliability and agreement between manual and automatic segmentations, volumes, and NPV/TFLs were assessed.
RESULTS: For treatment scans, the Dice similarity coefficients (DSC) between the manually and automatically obtained segmentations were 0.90 (uterus), 0.84 (NPV) and 0.74 (fibroid). Intraclass correlation coefficients (ICC) were 1.00 [0.99, 1.00] (uterus), 0.99 [0.98, 1.00] (NPV) and 0.98 [0.95, 0.99] (fibroid) between manually and automatically derived volumes. For manually and automatically derived NPV/TFLs, the mean difference was 5% [-41%, 51%] (ICC: 0.66 [0.32, 0.85]).
CONCLUSIONS: The algorithm presented in this study automatically calculates uterus volume, fibroid load, and NPVs, which could lead to more objective outcome quantification after MR-HIFU treatments of uterine fibroids in comparison to visual inspection. When robustness has been ascertained in a future study, this tool may eventually be employed in clinical practice to automatically measure the NPV/TFL after MR-HIFU procedures of uterine fibroids.
方法:对115例子宫肌瘤患者进行MRI扫描的专家手动分割,开发并评估了分割管道,筛查和/或接受MR-HIFU治疗。管道包含三个独立的神经网络,每个目标结构一个。管道的第一步是从对比增强(CE)-T1w扫描中分割子宫。该分割随后用于去除非子宫背景组织以进行NPV和纤维瘤分割。在接下来的步骤中,NPV从仅子宫CE-T1w扫描中分割。最后,根据仅子宫的T2w扫描对肌瘤进行分割。分割用于计算每个结构的体积。手动和自动分割之间的可靠性和协议,卷,和NPV/TFL进行评估。
结果:对于治疗扫描,手动和自动获得的切分之间的骰子相似系数(DSC)为0.90(子宫),0.84(净现值)和0.74(肌瘤)。组内相关系数(ICC)为1.00[0.99,1.00](子宫),手动和自动导出体积之间的0.99[0.98,1.00](NPV)和0.98[0.95,0.99](纤维瘤)。对于手动和自动导出的NPV/TFL,平均差异为5%[-41%,51%](ICC:0.66[0.32,0.85])。
结论:本研究中提出的算法自动计算子宫体积,肌瘤负荷,和NPV,与目视检查相比,这可能导致MR-HIFU治疗子宫肌瘤后更客观的结果量化。当在未来的研究中确定了稳健性时,该工具最终可用于临床实践,在子宫肌瘤MR-HIFU手术后自动测量NPV/TFL.