CT-ORG: A Dataset of CT Volumes With Multiple Organ Segmentations

This dataset consists of 140 computed tomography (CT) scans, each with five organs labeled in 3D: lung, bones, liver, kidneys and bladder. The brain is also labeled on the minority of scans which show it.
Patients were included based on the presence of lesions in one or more of the labeled organs. Most of the images exhibit liver lesions, both benign and malignant. Some also exhibit metastatic disease in other organs such as bones and lungs.
The images come from a wide variety of sources, including abdominal and full-body; contrast and non-contrast; low-dose and high-dose CT scans. 130 images are dedicated CTs, the remaining 10 are the CT component taken from PET-CT exams. This makes the dataset ideal for training and evaluating organ segmentation algorithms, which ought to perform well in a wide variety of imaging conditions.
The dataset includes large and easily-located organs such as the lungs, as well as small and difficult ones like the bladder. We hope the dataset will enable widespread adoption of multi-class organ segmentation, as well as competitive benchmarking of algorithms for it.
The data are divided into a testing set of 21 CT scans, and a training set of the remaining 119. For the training set, the lungs and bones were automatically segmented by morphological image processing. For the testing set, the lungs and bones were segmented manually. All other organs were segmented manually in both the training and testing sets. Manual segmentations were done with ITK-SNAP (https://www.itksnap.org), starting with semi-automatic active contour segmentation followed by manual clean-up. The source code for the morphological algorithms is available at:
- https://github.com/bbrister/ctOrganSegmentation.git
Many images were borrowed from the Liver Tumor Segmentation (LiTS) challenge, which the organizers have generously allowed us to distribute. For more information, see the following website and paper:
- https://lits-challenge.com
- Arxiv [1901.04056] The Liver Tumor Segmentation Benchmark (LiTS) (https://arxiv.org/abs/1901.04056)
This work was supported in part by grants from the National Cancer Institute, National Institutes of Health, 1U01CA190214 and 1U01CA187947.

本数据集包含140例计算机断层扫描（computed tomography，CT）影像，每例影像均对5个器官完成了3D标注：肺、骨骼、肝脏、肾脏与膀胱。在少数显示脑部的扫描影像中，同时完成了脑部标注。

入组患者的入选标准为其标注器官中存在一处或多处病灶。大部分影像可见良、恶性肝脏病灶；部分影像还可见骨骼、肺等其他器官的转移性病变。

本数据集影像来源多样，涵盖腹部及全身扫描、增强与平扫、低剂量与高剂量CT扫描。其中130例为专用CT扫描，剩余10例为PET-CT检查中的CT组分。该数据集适配性极强，可用于训练与评估器官分割算法，使其能够在多种成像条件下均获得优异表现。

本数据集既包含肺、骨骼这类体积较大、易于分割的器官，也涵盖膀胱这类体积较小、分割难度较高的器官。我们期望本数据集能够推动多类别器官分割技术的广泛应用，并为相关算法提供具有竞争力的基准测试平台。

本数据集划分为测试集与训练集：测试集包含21例CT扫描，剩余119例构成训练集。训练集的肺与骨骼采用形态学图像处理实现自动分割；测试集的肺与骨骼则通过手动标注完成。其余所有器官在训练集与测试集中均采用手动标注。所有手动标注均通过ITK-SNAP（https://www.itksnap.org）完成，流程为先采用半自动主动轮廓分割，再进行手动校正。形态学算法的源代码可于以下网址获取：
- https://github.com/bbrister/ctOrganSegmentation.git

本数据集的大量影像源自肝脏肿瘤分割（Liver Tumor Segmentation, LiTS）挑战赛，承蒙主办方慷慨许可予以分发。更多信息可参阅以下网站与论文：
- https://lits-challenge.com
- arXiv [1901.04056]《肝脏肿瘤分割基准（LiTS）》（https://arxiv.org/abs/1901.04056）

本研究部分受美国国家癌症研究所、美国国立卫生研究院资助，资助编号为1U01CA190214与1U01CA187947。

搜集汇总

背景与挑战

背景概述

CT-ORG数据集包含140个CT扫描，每个扫描标注了肺、骨骼、肝脏、肾脏和膀胱五个器官的3D分割标签，少数扫描还包括大脑标注。数据来源多样，涵盖不同扫描类型和条件，旨在训练和评估多类器官分割算法，适用于广泛成像场景，并分为119个训练扫描和21个测试扫描，部分分割采用自动和手动结合的方法。