Zhong Zhangnan, Huang Li, Feng Shi-Ting, Lin Haiwei, Wang Xinyue, Lu Baolan, Cao Kangyang, Li Xuehua, Huang Bingsheng
Medical AI Lab, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, 518060, China.
Neusoft Institute Guangdong, Foshan, 528225, China.
Sci Data. 2025 Mar 11;12(1):425. doi: 10.1038/s41597-025-04760-z.
Inflammatory bowel disease (IBD) is a recurrent bowel disease that usually requires magnetic resonance enterography (MRE) for diagnosis and monitoring. However, recognition of bowel segments from MRE images by a radiologist is challenging and time-consuming. Deep learning-based medical image segmentation has shown the potential to reduce manual effort and provide automated tools to assist in disease management; however, it requires a large-scale fine-annotated dataset for training. To address this gap, we collected MRE data, including half-Fourier acquisition single-shot turbo spin-echo(HASTE) sequences with coronal orientation, from 114 patients with IBD, who received 1600-2000 mL of 2.5% mannitol. The bowel images per patient were contoured and annotated into ten segments (stomach, duodenum, small intestine, appendix, cecum, ascending colon, transverse colon, descending colon, sigmoid colon, and rectum), with fine pixel-level annotations labeled by experienced radiologists. Furthermore, we validated the efficiency of several state-of-the-art segmentation methods using this dataset. This study established a high-quality, publicly available whole-bowel segment MR dataset with benchmark results and laid the groundwork for AI research on IBD.
炎症性肠病（IBD）是一种复发性肠道疾病，通常需要磁共振肠造影（MRE）来进行诊断和监测。然而，放射科医生从MRE图像中识别肠段具有挑战性且耗时。基于深度学习的医学图像分割已显示出减少人工工作量并提供自动化工具以协助疾病管理的潜力；然而，它需要一个大规模的精细标注数据集进行训练。为了弥补这一差距，我们收集了114例IBD患者的MRE数据，包括具有冠状位方向的半傅里叶采集单次激发快速自旋回波（HASTE）序列，这些患者接受了1600 - 2000 mL的2.5%甘露醇。对每位患者的肠道图像进行轮廓勾勒并标注为十个肠段（胃、十二指肠、小肠、阑尾、盲肠、升结肠、横结肠、降结肠、乙状结肠和直肠），由经验丰富的放射科医生进行精细的像素级标注。此外，我们使用该数据集验证了几种先进分割方法的效率。本研究建立了一个高质量、公开可用的全肠段MR数据集，并给出了基准结果，为IBD的人工智能研究奠定了基础。
