Salimi Yazdan, Mansouri Zahra, Shiri Isaac, Mainta Ismini, Zaidi Habib
From the Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva, Switzerland.
Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
Clin Nucl Med. 2025 Apr 1;50(4):289-300. doi: 10.1097/RLU.0000000000005685. Epub 2025 Jan 28.
The common approach for organ segmentation in hybrid imaging relies on coregistered CT (CTAC) images. This method, however, presents several limitations in real clinical workflows where mismatch between PET and CT images are very common. Moreover, low-dose CTAC images have poor quality, thus challenging the segmentation task. Recent advances in CT-less PET imaging further highlight the necessity for an effective PET organ segmentation pipeline that does not rely on CT images. Therefore, the goal of this study was to develop a CT-less multitracer PET segmentation framework.
We collected 2062 PET/CT images from multiple scanners. The patients were injected with either 18 F-FDG (1487) or 68 Ga-PSMA (575). PET/CT images with any kind of mismatch between PET and CT images were detected through visual assessment and excluded from our study. Multiple organs were delineated on CT components using previously trained in-house developed nnU-Net models. The segmentation masks were resampled to coregistered PET images and used to train 4 different deep learning models using different images as input, including noncorrected PET (PET-NC) and attenuation and scatter-corrected PET (PET-ASC) for 18 F-FDG (tasks 1 and 2, respectively using 22 organs) and PET-NC and PET-ASC for 68 Ga tracers (tasks 3 and 4, respectively, using 15 organs). The models' performance was evaluated in terms of Dice coefficient, Jaccard index, and segment volume difference.
The average Dice coefficient over all organs was 0.81 ± 0.15, 0.82 ± 0.14, 0.77 ± 0.17, and 0.79 ± 0.16 for tasks 1, 2, 3, and 4, respectively. PET-ASC models outperformed PET-NC models ( P < 0.05) for most of organs. The highest Dice values were achieved for the brain (0.93 to 0.96 in all 4 tasks), whereas the lowest values were achieved for small organs, such as the adrenal glands. The trained models showed robust performance on dynamic noisy images as well.
Deep learning models allow high-performance multiorgan segmentation for 2 popular PET tracers without the use of CT information. These models may tackle the limitations of using CT segmentation in PET/CT image quantification, kinetic modeling, radiomics analysis, dosimetry, or any other tasks that require organ segmentation masks.
混合成像中器官分割的常用方法依赖于配准的CT（CTAC）图像。然而，这种方法在实际临床工作流程中存在一些局限性，其中PET和CT图像之间的不匹配非常常见。此外，低剂量CTAC图像质量较差，给分割任务带来了挑战。无CT的PET成像的最新进展进一步凸显了开发一种不依赖CT图像的有效PET器官分割流程的必要性。因此，本研究的目的是开发一种无CT的多示踪剂PET分割框架。
我们从多个扫描仪收集了2062例PET/CT图像。患者注射了18F-FDG（1487例）或68Ga-PSMA（575例）。通过视觉评估检测PET和CT图像之间存在任何不匹配的PET/CT图像，并将其排除在我们的研究之外。使用先前训练的内部开发的nnU-Net模型在CT组件上勾勒出多个器官。将分割掩码重新采样到配准的PET图像上，并使用不同的图像作为输入来训练4种不同的深度学习模型，包括18F-FDG的未校正PET（PET-NC）和衰减及散射校正PET（PET-ASC）（任务1和2，分别使用22个器官）以及68Ga示踪剂的PET-NC和PET-ASC（任务3和4，分别使用15个器官）。根据Dice系数、Jaccard指数和分割体积差异评估模型的性能。
任务1、2、3和4中所有器官的平均Dice系数分别为0.81±0.15、0.82±0.14、0.77±0.17和0.79±0.16。对于大多数器官，PET-ASC模型的表现优于PET-NC模型（P<0.05）。大脑的Dice值最高（所有4个任务中为0.93至0.96），而肾上腺等小器官的Dice值最低。训练后的模型在动态噪声图像上也表现出稳健的性能。
深度学习模型允许在不使用CT信息的情况下对两种常用PET示踪剂进行高性能的多器官分割。这些模型可以解决在PET/CT图像定量、动力学建模、放射组学分析、剂量测定或任何其他需要器官分割掩码的任务中使用CT分割的局限性。
