Intra-Individual Reproducibility of Automated Abdominal Organ Segmentation—Performance of TotalSegmentator Compared to Human Readers and an Independent nnU-Net Model

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Lorraine Abel, Jakob Wasserthal, Manfred T. Meyer, Jan Vosshenrich, Shan Yang, Ricardo Donners, Markus Obmann, Daniel Boll, Elmar Merkle, Hanns-Christian Breit, Martin Segeroth
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引用次数: 0

Abstract

The purpose of this study is to assess segmentation reproducibility of artificial intelligence-based algorithm, TotalSegmentator, across 34 anatomical structures using multiphasic abdominal CT scans comparing unenhanced, arterial, and portal venous phases in the same patients. A total of 1252 multiphasic abdominal CT scans acquired at our institution between January 1, 2012, and December 31, 2022, were retrospectively included. TotalSegmentator was used to derive volumetric measurements of 34 abdominal organs and structures from the total of 3756 CT series. Reproducibility was evaluated across three contrast phases per CT and compared to two human readers and an independent nnU-Net trained on the BTCV dataset. Relative deviation in segmented volumes and absolute volume deviations (AVD) were reported. Volume deviation within 5% was considered reproducible. Thus, non-inferiority testing was conducted using a 5% margin. Twenty-nine out of 34 structures had volume deviations within 5% and were considered reproducible. Volume deviations for the adrenal glands, gallbladder, spleen, and duodenum were above 5%. Highest reproducibility was observed for bones (− 0.58% [95% CI: − 0.58, − 0.57]) and muscles (− 0.33% [− 0.35, − 0.32]). Among abdominal organs, volume deviation was 1.67% (1.60, 1.74). TotalSegmentator outperformed the reproducibility of the nnU-Net trained on the BTCV dataset with an AVD of 6.50% (6.41, 6.59) vs. 10.03% (9.86, 10.20; p < 0.0001), most notably in cases with pathologic findings. Similarly, TotalSegmentator’s AVD between different contrast phases was superior compared to the interreader AVD for the same contrast phase (p = 0.036). TotalSegmentator demonstrated high intra-individual reproducibility for most abdominal structures in multiphasic abdominal CT scans. Although reproducibility was lower in pathologic cases, it outperforms both human readers and a nnU-Net trained on the BTCV dataset.

Abstract Image

腹部器官自动分割的个体内再现性--TotalSegmentator 与人类阅读器和独立 nnU-Net 模型的性能比较
本研究旨在评估基于人工智能的算法 TotalSegmentator 在 34 个解剖结构中的分割再现性,该算法使用多相腹部 CT 扫描,比较同一患者的未增强、动脉和门静脉相。我们回顾性地纳入了本机构在 2012 年 1 月 1 日至 2022 年 12 月 31 日期间获得的 1252 份多相腹部 CT 扫描。使用 TotalSegmentator 从总共 3756 个 CT 系列中得出 34 个腹部器官和结构的容积测量值。对每台 CT 的三个对比阶段的再现性进行了评估,并与两名人类阅读器和在 BTCV 数据集上训练的独立 nnU-Net 进行了比较。报告了分割体积的相对偏差和绝对体积偏差(AVD)。体积偏差在 5% 以内被认为是可重复的。因此,非劣效性测试使用 5%的余量进行。在 34 个结构中,有 29 个结构的体积偏差在 5%以内,被认为是可重复的。肾上腺、胆囊、脾脏和十二指肠的体积偏差超过了 5%。骨骼(- 0.58% [95% CI: - 0.58, - 0.57])和肌肉(- 0.33% [- 0.35, - 0.32])的再现性最高。在腹部器官中,体积偏差为 1.67% (1.60, 1.74)。TotalSegmentator 的再现性优于在 BTCV 数据集上训练的 nnU-Net,其 AVD 为 6.50% (6.41, 6.59) vs. 10.03% (9.86, 10.20; p < 0.0001),在有病理结果的病例中尤为明显。同样,TotalSegmentator 在不同对比阶段之间的 AVD 也优于同一对比阶段的读片机间 AVD(p = 0.036)。在多相腹部 CT 扫描中,TotalSegmentator 对大多数腹部结构显示出较高的个体内再现性。虽然在病理病例中重现性较低,但它的表现优于人类阅读器和在 BTCV 数据集上训练的 nnU-Net。
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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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