Putz Florian, Beirami Sogand, Schmidt Manuel Alexander, May Matthias Stefan, Grigo Johanna, Weissmann Thomas, Schubert Philipp, Höfler Daniel, Gomaa Ahmed, Hassen Ben Tkhayat, Lettmaier Sebastian, Frey Benjamin, Gaipl Udo S, Distel Luitpold V, Semrau Sabine, Bert Christoph, Fietkau Rainer, Huang Yixing
Department of Radiation Oncology, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Universitaetsstraße 27, 91054, Erlangen, Germany.
Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Strahlenther Onkol. 2025 Mar;201(3):255-265. doi: 10.1007/s00066-024-02313-8. Epub 2024 Nov 6.
Promptable foundation auto-segmentation models like Segment Anything (SA, Meta AI, New York, USA) represent a novel class of universal deep learning auto-segmentation models that could be employed for interactive tumor auto-contouring in RT treatment planning.
Segment Anything was evaluated in an interactive point-to-mask auto-segmentation task for glioma brain tumor auto-contouring in 16,744 transverse slices from 369 MRI datasets (BraTS 2020 dataset). Up to nine interactive point prompts were automatically placed per slice. Tumor boundaries were auto-segmented on contrast-enhanced T1w sequences. Out of the three auto-contours predicted by SA, accuracy was evaluated for the contour with the highest calculated IoU (Intersection over Union, "oracle mask," simulating interactive model use with selection of the best tumor contour) and for the tumor contour with the highest model confidence ("suggested mask").
Mean best IoU (mbIoU) using the best predicted tumor contour (oracle mask) in full MRI slices was 0.762 (IQR 0.713-0.917). The best 2D mask was achieved after a mean of 6.6 interactive point prompts (IQR 5-9). Segmentation accuracy was significantly better for high- compared to low-grade glioma cases (mbIoU 0.789 vs. 0.668). Accuracy was worse using the suggested mask (0.572). Stacking best tumor segmentations from transverse MRI slices, mean 3D Dice score for tumor auto-contouring was 0.872, which was improved to 0.919 by combining axial, sagittal, and coronal contours.
The Segment Anything foundation segmentation model can achieve high accuracy for glioma brain tumor segmentation in MRI datasets. The results suggest that foundation segmentation models could facilitate RT treatment planning when properly integrated in a clinical application.
像Segment Anything（SA，美国纽约Meta AI公司）这样的可提示基础自动分割模型代表了一类新型的通用深度学习自动分割模型，可用于放射治疗计划中的交互式肿瘤自动轮廓勾画。
在来自369个MRI数据集（BraTS 2020数据集）的16744个横断面上，对Segment Anything进行了用于脑胶质瘤自动轮廓勾画的交互式点到掩码自动分割任务评估。每个切片最多自动放置9个交互式点提示。在对比增强T1w序列上自动分割肿瘤边界。在SA预测的三个自动轮廓中，对计算出的交并比（IoU）最高的轮廓（“神谕掩码”，模拟通过选择最佳肿瘤轮廓使用交互式模型）和模型置信度最高的肿瘤轮廓（“建议掩码”）评估准确性。
在完整的MRI切片中，使用最佳预测肿瘤轮廓（神谕掩码）的平均最佳IoU（mbIoU）为0.762（四分位距0.713 - 0.917）。平均6.6个交互式点提示（四分位距5 - 9）后获得最佳二维掩码。与低级别胶质瘤病例相比，高级别胶质瘤病例的分割准确性明显更高（mbIoU分别为0.789和0.668）。使用建议掩码时准确性更差（0.572）。将横断MRI切片的最佳肿瘤分割结果叠加起来，肿瘤自动轮廓勾画的平均三维骰子分数为0.872，通过结合轴向、矢状和冠状轮廓可提高到0.919。
Segment Anything基础分割模型在MRI数据集中对脑胶质瘤分割可实现高精度。结果表明，基础分割模型在正确集成到临床应用中时可促进放射治疗计划。
