Comparison of organ volumes and standardized uptake values in [18F]FDG-PET/CT images using MOOSE and TotalSegmentator to segment CT images

IF 3.2 2区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Medical physics Pub Date : 2025-09-24 DOI:10.1002/mp.70025

Julie Auriac, Christophe Nioche, Narinée Hovhannisyan-Baghdasarian, Charlotte Loisel, Romain-David Seban, Nina Jehanno, Lalith Kumar Shiyam Sundar, Thomas Beyer, Irène Buvat, Fanny Orlhac

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Abstract

Background

Manual segmentation of organs from PET/CT images is a time-consuming and highly operator-dependent task. Open software solutions are now available to automatically segment all major anatomical structures in CT images.

Purpose

We compared the volumes and standardized uptake values (SUV) extracted from [18F]FDG-PET/CT patient scans for 33 anatomical structures segmented using two deep learning (DL) algorithms to determine if they are interchangeable.

Methods

Baseline [18F]FDG-PET/CT images were collected retrospectively for 315 women with metastatic breast cancer. A total of 33 anatomical volumes of interest (VOI) were segmented from the whole-body CT scans using both MOOSE v.3.0.14 and TotalSegmentator v.2.0.5 and copied onto the corresponding PET images. For each VOI, the volume from the CT image and SUVmax, SUVpeak and SUVmean from the PET image were extracted. The resulting values were compared using the relative difference for each feature.

Results

Following DL segmentation, resulting organ volumes differed by less than 10% for 19/33 organs in more than 80% (252/315) of patients. Four organs were segmented with volume differences greater than 20% in 1/5th of patients: bladder (48%, p < 0.0001), portal and splenic veins (34%, p < 0.0001), thyroid (16%, p < 0.0001), adrenal glands (15%, p < 0.0001). SUVmax and SUVpeak were affected by the choice of DL algorithms, with values differing by less than 10% in more than 80% of patients for only 16 and 19 out of 33 organs, respectively. In contrast, SUVmean was less affected with differences of less than 10% in more than 80% of patients for all anatomical structures, except the bladder, lungs and skull.

Conclusions

The two software tools produce similar results in volume estimates for most anatomical structures. SUVmean is less dependent on the segmentation algorithm than SUVmax and SUVpeak and shows excellent reproducibility for all anatomical structures studied except for the bladder, the lungs and the skull.

Abstract Image

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使用MOOSE和TotalSegmentator对CT图像进行分割，比较[18F]FDG-PET/CT图像中器官体积和标准化摄取值。

背景：从PET/CT图像中手动分割器官是一项耗时且高度依赖操作者的任务。开放的软件解决方案现在可以自动分割所有主要解剖结构的CT图像。目的：我们比较了从[18F]FDG-PET/CT患者扫描中提取的体积和标准化摄取值（SUV），使用两种深度学习（DL）算法对33个解剖结构进行分割，以确定它们是否可互换。方法：回顾性收集315例转移性乳腺癌患者的FDG-PET/CT基线影像[18F]。使用MOOSE v.3.0.14和TotalSegmentator v.2.0.5从全身CT扫描中分割出33个感兴趣的解剖体积（VOI），并复制到相应的PET图像上。对于每个VOI，提取CT图像的体积和PET图像的SUVmax、SUVpeak和SUVmean。使用每个特征的相对差值对结果值进行比较。结果：在超过80%（252/315）的患者中，经过DL分割后，33个器官中有19个器官的体积差异小于10%。在1/5的患者中，有4个器官的分割体积差异大于20%：膀胱（48%,p）。结论：这两种软件工具对大多数解剖结构的体积估计结果相似。与SUVmax和SUVpeak相比，SUVmean对分割算法的依赖程度较低，并且对除膀胱、肺和颅骨外的所有解剖结构都具有良好的再现性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Medical physics 医学-核医学

CiteScore

6.80

自引率

15.80%

发文量

660

审稿时长

1.7 months

期刊介绍： Medical Physics publishes original, high impact physics, imaging science, and engineering research that advances patient diagnosis and therapy through contributions in 1) Basic science developments with high potential for clinical translation 2) Clinical applications of cutting edge engineering and physics innovations 3) Broadly applicable and innovative clinical physics developments Medical Physics is a journal of global scope and reach. By publishing in Medical Physics your research will reach an international, multidisciplinary audience including practicing medical physicists as well as physics- and engineering based translational scientists. We work closely with authors of promising articles to improve their quality.