Kaneko Yuta, Miwa Kenta, Yamao Tensho, Miyaji Noriaki, Nishii Ryuichi, Yamazaki Kana, Nishikawa Noriko, Yusa Masanori, Higashi Tatsuya
Department of Radiology, Fukushima Medical University Hospital, 1 Hikarigaoka, Fukushima, Fukushima, 960-1247, Japan.
Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University, 10-6 Sakaemachi, Fukushima-shi, Fukushima, 960- 8516, Japan.
Phys Eng Sci Med. 2025 Jul 15. doi: 10.1007/s13246-025-01595-1.
Organ segmentation using F-FDG PET images alone has not been extensively explored. Segmentation based methods based on deep learning (DL) have traditionally relied on CT or MRI images, which are vulnerable to alignment issues and artifacts. This study aimed to develop a DL approach for segmenting the entire liver based solely on F-FDG PET images. We analyzed data from 120 patients who were assessed using F-FDG PET. A three-dimensional (3D) U-Net model from nnUNet and preprocessed PET images served as DL and input images, respectively, for the model. The model was trained with 5-fold cross-validation on data from 100 patients, and segmentation accuracy was evaluated on an independent test set of 20 patients. Accuracy was assessed using Intersection over Union (IoU), Dice coefficient, and liver volume. Image quality was evaluated using mean (SUVmean) and maximum (SUVmax) standardized uptake value and signal-to-noise ratio (SNR). The model achieved an average IoU of 0.89 and an average Dice coefficient of 0.94 based on test data from 20 patients, indicating high segmentation accuracy. No significant discrepancies in image quality metrics were identified compared with ground truth. Liver regions were accurately extracted from F-FDG PET images which allowed rapid and stable evaluation of liver uptake in individual patients without the need for CT or MRI assessments.
仅使用F-FDG PET图像进行器官分割尚未得到广泛探索。基于深度学习（DL）的分割方法传统上依赖于CT或MRI图像，而这些图像容易出现配准问题和伪影。本研究旨在开发一种仅基于F-FDG PET图像分割整个肝脏的DL方法。我们分析了120例接受F-FDG PET评估的患者的数据。来自nnUNet的三维（3D）U-Net模型和预处理后的PET图像分别用作模型的DL和输入图像。该模型在100例患者的数据上进行了5折交叉验证训练，并在20例患者的独立测试集上评估了分割准确性。使用交并比（IoU）、Dice系数和肝脏体积评估准确性。使用平均（SUVmean）和最大（SUVmax）标准化摄取值以及信噪比（SNR）评估图像质量。基于20例患者的测试数据，该模型的平均IoU为0.89，平均Dice系数为0.94，表明分割准确性较高。与真实情况相比，未发现图像质量指标存在显著差异。从F-FDG PET图像中准确提取了肝脏区域，这使得无需CT或MRI评估即可快速、稳定地评估个体患者的肝脏摄取情况。
