Abel Lorraine, Wasserthal Jakob, Meyer Manfred T, Vosshenrich Jan, Yang Shan, Donners Ricardo, Obmann Markus, Boll Daniel, Merkle Elmar, Breit Hanns-Christian, Segeroth Martin
Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
J Imaging Inform Med. 2025 Jun;38(3):1617-1627. doi: 10.1007/s10278-024-01265-w. Epub 2024 Sep 18.
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.
本研究的目的是使用多期腹部CT扫描，在同一患者中比较平扫、动脉期和门静脉期，评估基于人工智能的算法TotalSegmentator在34个解剖结构上的分割可重复性。回顾性纳入了2012年1月1日至2022年12月31日在本机构采集的总共1252例多期腹部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体积测量值。在每个CT的三个对比期评估可重复性，并与两名人类阅片者以及在BTCV数据集上训练的独立nnU-Net进行比较。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，使用5%的 margin 进行非劣效性测试。34个结构中有29个的体积偏差在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高于5%。骨骼（-0.58% [95% CI：-0.58，-0.57]）和肌肉（-0.33% [-0.35，-0.32]）的可重复性最高。在腹部器官中，体积偏差为1.67%（1.60，1.74）。TotalSegmentator的AVD为6.50%（6.41，6.59），优于在BTCV数据集上训练的nnU-Net的可重复性，后者的AVD为10.03%（9.86，10.20；p < 0.0001），在有病理发现的病例中尤为明显。同样，TotalSegmentator在不同对比期之间的AVD优于同一对比期的阅片者间AVD（p = 0.036）。TotalSegmentator在多期腹部CT扫描中对大多数腹部结构表现出较高的个体内可重复性。尽管在病理病例中可重复性较低，但它优于人类阅片者和在BTCV数据集上训练的nnU-Net。
