Lee Sangjune L, Yadav Poonam, Li Yin, Meudt Jason J, Strang Jessica, Hebel Dustin, Alfson Alyx, Olson Stephanie J, Kruser Tera R, Smilowitz Jennifer B, Borchert Kailee, Loritz Brianne, Gharzai Laila, Karimpour Shervin, Bayouth John, Bassetti Michael F
Division of Radiation Oncology, Arthur Child Comprehensive Cancer Centre3395 Hospital Drive NW, Calgary, Alberta, T2N 5G2, Canada.
Department of Radiation Oncology, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, 675 North Saint Clair Street 21st Floor, Chicago, IL 60611, USA.
Data Brief. 2024 Nov 26;57:111159. doi: 10.1016/j.dib.2024.111159. eCollection 2024 Dec.
Integrated MRI and linear accelerator systems (MR-Linacs) provide superior soft tissue contrast, and the capability of adapting radiotherapy plans to changes in daily anatomy. In this dataset, serial MRIs of the abdomen of patients undergoing radiotherapy were collected and the luminal gastro-intestinal tract was segmented to support an online segmentation algorithm competition. This dataset may be further utilized by radiation oncologists, medical physicists, and data scientists to further improve auto segmentation algorithms.
Serial 0.35T MRIs from patients who were treated on an MR-Linac for tumors located in the abdomen were collected. The stomach, small intestine and large intestine were manually segmented on all MRIs by a team of annotators under the supervision of a board-certified radiation oncologist. Annotator segmentations were validated on 4 representative abdominal MRIs by comparing to the radiation oncologist's contours using 3D Hausdorff distance and 3D Dice coefficient metrics.
The dataset includes 467 de-identified scans and their contours from 107 patients. Each patient underwent 1-5 MRI scans of the abdomen. Most of the scans consisted of 144 axial slices with a pixel resolution of 1.5 × 1.5 × 3 mm, leading to 67,248 total slices in the dataset. Images in DICOM format were converted into Portable Graphics Format (PNG) files. Each Portable Graphics Format (PNG) image file stored a slice of the scan, with pixels recorded in 16 bits to cover the full range of intensity values. DICOM-RT segmentations were converted into Comma-Separated Values (CSV) format. Data including images and the annotations is publicly available at https://www.kaggle.com/ds/3577354.
While manual segmentations are subject to bias and inter-observer variability, the dataset has been used for the UW-Madison GI Tract Image Segmentation Challenge hosted by Kaggle and may be used for ongoing segmentation algorithm development and potentially for dose accumulation algorithms.
集成式磁共振成像与直线加速器系统（MR-Linac）能提供卓越的软组织对比度，以及使放射治疗计划适应每日解剖结构变化的能力。在此数据集中，收集了接受放射治疗患者腹部的系列磁共振成像，并对胃肠道管腔进行了分割，以支持一场在线分割算法竞赛。放射肿瘤学家、医学物理学家和数据科学家可进一步利用此数据集来进一步改进自动分割算法。
收集了在MR-Linac上接受腹部肿瘤治疗患者的系列(0.35T)磁共振成像。一组注释人员在一名获得委员会认证的放射肿瘤学家的监督下，对所有磁共振成像上的胃、小肠和大肠进行了手动分割。通过使用三维豪斯多夫距离和三维骰子系数指标，将注释人员的分割结果与放射肿瘤学家的轮廓进行比较，在4幅具有代表性的腹部磁共振成像上对注释人员的分割结果进行了验证。
该数据集包括来自107名患者的467份去识别化扫描及其轮廓。每位患者接受了1至5次腹部磁共振成像扫描。大多数扫描由144个轴向切片组成，像素分辨率为(1.5×1.5×3)毫米，数据集中总切片数达67248片。DICOM格式的图像被转换为便携式图形格式（PNG）文件。每个便携式图形格式（PNG）图像文件存储扫描的一个切片，像素以16位记录，以涵盖强度值的全范围。DICOM-RT分割结果被转换为逗号分隔值（CSV）格式。包括图像和注释在内的数据可在https://www.kaggle.com/ds/3577354上公开获取。
虽然手动分割存在偏差和观察者间的变异性，但该数据集已用于由Kaggle主办的威斯康星大学麦迪逊分校胃肠道图像分割挑战赛，并且可用于正在进行的分割算法开发，以及可能用于剂量累积算法。
