Park Junghoan, Park Sungeun, Chung Han-Jae, Lee Da In, Kim Jong-Min, Kim Se Hyung, Choe Eun Kyung, Park Kyu Joo, Yoon Soon Ho
Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea.
Department of Radiology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea.
Eur Radiol. 2025 May 2. doi: 10.1007/s00330-025-11623-z.
To develop a deep neural network for automatic bowel segmentation and assess its applicability for estimating large bowel length (LBL) in individuals with constipation.
We utilized contrast-enhanced and non-enhanced abdominal, chest, and whole-body CT images for model development. External testing involved paired pre- and post-contrast abdominal CT images from another hospital. We developed 3D nnU-Net models to segment the gastrointestinal tract and separate it into the esophagus, stomach, small bowel, and large bowel. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC) based on radiologists' segmentation. We employed the network to estimate LBL in individuals having abdominal CT for health check-ups, and the height-corrected LBL was compared between groups with and without constipation.
One hundred thirty-three CT scans (88 patients; age, 63.6 ± 10.6 years; 39 men) were used for model development, and 60 for external testing (30 patients; age, 48.9 ± 15.8 years; 16 men). In the external dataset, the mean DSC for the entire gastrointestinal tract was 0.985 ± 0.008. The mean DSCs for four-part separation exceeded 0.95, outperforming TotalSegmentator, except for the esophagus (DSC, 0.807 ± 0.173). For LBL measurements, 100 CT scans from 51 patients were used (age, 67.0 ± 6.9 years; 59 scans from men; 59 with constipation). The height-corrected LBL were significantly longer in the constipation group on both per-exam (79.1 ± 12.4 vs 88.8 ± 15.8 cm/m, p = 0.001) and per-subject basis (77.6 ± 13.6 vs 86.9 ± 17.1 cm/m, p = 0.04).
Our model accurately segmented the entire gastrointestinal tract and its major compartments from CT scans and enabled the noninvasive estimation of LBL in individuals with constipation.
Questions Automated bowel segmentation is a first step for algorithms, including bowel tracing and length measurement, but the complexity of the gastrointestinal tract limits its accuracy. Findings Our 3D nnU-Net model showed high performance in segmentation and four-part separation of the GI tract (DSC > 0.95), except for the esophagus. Clinical relevance Our model accurately segments the gastrointestinal tract and separates it into major compartments. Our model potentially has use in various clinical applications, including semi-automated measurement of LBL in individuals with constipation.
开发一种用于自动肠道分割的深度神经网络，并评估其在估计便秘患者大肠长度（LBL）方面的适用性。
我们利用增强和未增强的腹部、胸部及全身CT图像进行模型开发。外部测试使用了来自另一家医院的对比剂注射前后的配对腹部CT图像。我们开发了3D nnU-Net模型来分割胃肠道，并将其分为食管、胃、小肠和大肠。基于放射科医生的分割结果，使用Dice相似系数（DSC）评估分割准确性。我们使用该网络估计接受腹部CT健康检查的个体的LBL，并比较有无便秘组之间的身高校正LBL。
133例CT扫描（88例患者；年龄63.6±10.6岁；39例男性）用于模型开发，60例用于外部测试（30例患者；年龄48.9±15.8岁；16例男性）。在外部数据集中，整个胃肠道的平均DSC为0.985±0.008。除食管外（DSC，0.807±0.173），四部分分割的平均DSC超过0.95，优于TotalSegmentator。对于LBL测量，使用了51例患者的100例CT扫描（年龄67.0±6.9岁；59例男性扫描；59例便秘患者）。在每次检查（79.1±12.4 vs 88.8±15.8 cm/m，p = 0.001）和每个受试者基础上（77.6±13.6 vs 86.9±17.1 cm/m，p = 0.04），便秘组的身高校正LBL均显著更长。
我们的模型能从CT扫描中准确分割整个胃肠道及其主要部分，并能对便秘患者进行无创LBL估计。
问题自动肠道分割是包括肠道追踪和长度测量在内的算法的第一步，但胃肠道的复杂性限制了其准确性。发现我们的3D nnU-Net模型在胃肠道分割和四部分分离方面表现出高性能（DSC>0.95），食管除外。临床意义我们的模型能准确分割胃肠道并将其分为主要部分。我们的模型可能在各种临床应用中有用，包括对便秘患者进行LBL的半自动测量。
