Interobserver ground-truth variability limits performance of automated glioblastoma segmentation on [18F]FET PET.

IF 3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Selene De Sutter, Ine Dirks, Laurens Raes, Wietse Geens, Hendrik Everaert, Sophie Bourgeois, Johnny Duerinck, Jef Vandemeulebroucke
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引用次数: 0

Abstract

Background: Positron emission tomography (PET) with a [18F]fluoroethyl)-L-tyrosine ([18F]FET) tracer is of growing importance in the management of glioblastoma for the estimation of tumor extent and extraction of diagnostic and prognostic parameters. Robust and accurate glioblastoma segmentation methods are essential to maximize the benefits of this imaging modality. Given the importance of setting the foreground threshold during manual tumor delineation, this study investigates the added value of incorporating such prior knowledge to guide the automated segmentation and improve performance. Two segmentation networks were trained based on the nnU-Net guidelines: one with the [18F]FET PET image as sole input, and one with an additional input channel for the threshold map. For the latter, we investigate the benefit of manually obtained thresholds and explore automated prediction and generation of such maps. A fully automated pipeline was constructed by selecting the best performing threshold prediction approach and cascading this with the tumor segmentation model.

Results: The proposed two-channel network shows increased performance with guidance of threshold maps originating from the same reader whose ground-truth tumor label the prediction is compared to (DSC = 0.901). When threshold maps were generated by a different reader, performance reverted to levels comparable to the one-channel network and inter-reader variability. The proposed full pipeline achieves results on par with current state of the art (DSC = 0.807).

Conclusions: Incorporating a threshold map can significantly improve tumor segmentation performance when it aligns well with the ground-truth label. However, the current inability to reliably reproduce these maps-both manually and automatically-or the ground-truth tumor labels, restricts the achievable accuracy for automated glioblastoma segmentation on [18F]FET PET, highlighting the need for more consistent definitions of such ground-truth delineations.

观察者间的真值变异性限制了在[18F]场效应晶体管PET上自动分割胶质母细胞瘤的性能。
背景:使用[18F]氟乙基- l -酪氨酸([18F]FET)示踪剂的正电子发射断层扫描(PET)在胶质母细胞瘤的治疗中越来越重要,因为它可以估计肿瘤的范围,提取诊断和预后参数。稳健和准确的胶质母细胞瘤分割方法是至关重要的,以最大限度地发挥这种成像方式的好处。考虑到在人工肿瘤分割过程中设置前景阈值的重要性,本研究探讨了结合这种先验知识来指导自动分割和提高性能的附加价值。基于nnU-Net准则训练了两个分割网络:一个以[18F]FET PET图像作为唯一输入,另一个具有用于阈值图的附加输入通道。对于后者,我们研究了人工获得阈值的好处,并探索了这种地图的自动预测和生成。通过选择性能最好的阈值预测方法并与肿瘤分割模型级联,构建了一个全自动流水线。结果:所提出的双通道网络在阈值图的引导下表现出更高的性能,阈值图来自于与预测相比较的同一阅读器(DSC = 0.901)。当阈值映射由不同的读取器生成时,性能恢复到与单通道网络和读取器间可变性相当的水平。拟议的完整管道达到与当前技术水平相当的结果(DSC = 0.807)。结论:结合阈值图可以显著提高肿瘤分割性能,当它与真值标签很好地对齐时。然而,目前无法可靠地复制这些地图(无论是手动还是自动)或基础真值肿瘤标签,限制了在[18F]FET PET上实现胶质母细胞瘤自动分割的准确性,这突出了对这种基础真值描绘的更一致定义的需求。
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来源期刊
EJNMMI Physics
EJNMMI Physics Physics and Astronomy-Radiation
CiteScore
6.70
自引率
10.00%
发文量
78
审稿时长
13 weeks
期刊介绍: EJNMMI Physics is an international platform for scientists, users and adopters of nuclear medicine with a particular interest in physics matters. As a companion journal to the European Journal of Nuclear Medicine and Molecular Imaging, this journal has a multi-disciplinary approach and welcomes original materials and studies with a focus on applied physics and mathematics as well as imaging systems engineering and prototyping in nuclear medicine. This includes physics-driven approaches or algorithms supported by physics that foster early clinical adoption of nuclear medicine imaging and therapy.
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