Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.
Pediatric chest-abdomen-pelvis and abdomen-pelvis CT images with expert organ contours.
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由于儿科培训数据的可用性有限，迄今为止器官自动分割的努力主要集中在成人人群中。儿科患者可能会对器官分割提出额外的挑战。本文描述了359个儿童胸腹骨盆和腹部骨盆计算机断层扫描（CT）图像的数据集，其中包含多达29个解剖器官结构的专家轮廓，以帮助评估和开发儿童CT成像的自动分割算法。数据集收集由DICOM格式的180名男性和179名女性儿童胸部-腹部-骨盆或腹部-骨盆检查的轴向CT图像组成，这些图像来自威斯康星州儿童医院的三台CT扫描仪中的一台。数据集代表基于常规临床适应症的随机儿科病例。受试者的年龄从5天到16岁不等，平均年龄为7岁。CT采集、对比和重建方案因扫描仪型号和患者而异，其规格可在DICOM标头中找到。专家轮廓被手动标记为每个受试者多达29个器官结构。并非所有的轮廓都适用于所有的主体，由于有限的视野或不可靠的轮廓，由于高噪声。这些数据可在癌症影像档案（TCIA） （https://www.cancerimagingarchive.net/）儿科ct - seg集合下获得。每个受试者的轴向CT图像切片以DICOM格式提供。专家轮廓存储在每个主题的单个DICOM RTSTRUCT文件中。等高线名称如表2所示。该数据集将能够评估和开发针对儿科人群的器官自动分割算法，这些人群在不同年龄的器官形状和大小上表现出变化。从CT图像中自动分割器官有许多应用，包括放射治疗、诊断任务、手术计划和患者特异性器官剂量估计。
Organ autosegmentation efforts to date have largely been focused on adult populations, due to limited availability of pediatric training data. Pediatric patients may present additional challenges for organ segmentation. This paper describes a dataset of 359 pediatric chest-abdomen-pelvis and abdomen-pelvis Computed Tomography (CT) images with expert contours of up to 29 anatomical organ structures to aid in the evaluation and development of autosegmentation algorithms for pediatric CT imaging. The dataset collection consists of axial CT images in DICOM format of 180 male and 179 female pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from one of three CT scanners at Children’s Wisconsin. The datasets represent random pediatric cases based upon routine clinical indications. Subjects ranged in age from 5 days to 16 years, with a mean age of seven years. The CT acquisition, contrast, and reconstruction protocols varied across the scanner models and patients, with specifications available in the DICOM headers. Expert contours were manually labeled for up to 29 organ structures per subject. Not all contours are available for all subjects, due to limited field of view or unreliable contouring due to high noise. The data are available on The Cancer Imaging Archive (TCIA) (https://www.cancerimagingarchive.net/) under the collection Pediatric-CT-SEG. The axial CT image slices for each subject are available in DICOM format. The expert contours are stored in a single DICOM RTSTRUCT file for each subject. The contour names are as listed in Table 2. This dataset will enable the evaluation and development of organ autosegmentation algorithms for pediatric populations, which exhibit variations in organ shape and size across age. Automated organ segmentation from CT images has numerous applications including radiation therapy, diagnostic tasks, surgical planning, and patient-specific organ dose estimation.