This manuscript describes a dataset of thoracic cavity segmentations and discrete pleural effusion segmentations we have annotated on 402 computed tomography (CT) scans acquired from patients with non‐small cell lung cancer. The segmentation of these anatomic regions precedes fundamental tasks in image analysis pipelines such as lung structure segmentation, lesion detection, and radiomics feature extraction. Bilateral thoracic cavity volumes and pleural effusion volumes were manually segmented on CT scans acquired from The Cancer Imaging Archive “NSCLC Radiomics” data collection. Four hundred and two thoracic segmentations were first generated automatically by a U‐Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student to include the complete thoracic cavity (normal, pathologic, and atelectatic lung parenchyma, lung hilum, pleural effusion, fibrosis, nodules, tumor, and other anatomic anomalies), and revised by a radiation oncologist or a radiologist. Seventy‐eight pleural effusions were manually segmented by a medical student and revised by a radiologist or radiation oncologist. Interobserver agreement between the radiation oncologist and radiologist corrections was acceptable. All expert‐vetted segmentations are publicly available in NIfTI format through The Cancer Imaging Archive at https://doi.org/10.7937/tcia.2020.6c7y‐gq39. Tabular data detailing clinical and technical metadata linked to segmentation cases are also available. Thoracic cavity segmentations will be valuable for developing image analysis pipelines on pathologic lungs — where current automated algorithms struggle most. In conjunction with gross tumor volume segmentations already available from “NSCLC Radiomics,” pleural effusion segmentations may be valuable for investigating radiomics profile differences between effusion and primary tumor or training algorithms to discriminate between them.

中文翻译：

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PleThora：患病肺部的胸腔积液和胸腔分割，用于胸部 CT 处理流程的基准测试。


本手稿描述了胸腔分割和离散胸腔积液分割的数据集，我们对从非小细胞肺癌患者采集的 402 幅计算机断层扫描 (CT) 扫描进行了注释。这些解剖区域的分割先于图像分析流程中的基本任务，例如肺结构分割、病变检测和放射组学特征提取。根据从癌症成像档案馆“NSCLC 放射组学”数据收集中获取的 CT 扫描，对双侧胸腔体积和胸腔积液体积进行手动分割。首先通过基于 U-Net 的算法自动生成 402 个胸部分割，该算法在无癌症的胸部 CT 上进行训练，然后由医学生手动校正，以包括完整的胸腔（正常、病理和肺不张的肺实质、肺门、胸膜）。积液、纤维化、结节、肿瘤和其他解剖异常），并由放射肿瘤科医生或放射科医生修改。七十八例胸腔积液由一名医学生手动分割，并由放射科医生或放射肿瘤科医生进行修改。放射肿瘤科医生和放射科医生校正之间的观察者间一致性是可以接受的。所有经过专家审查的分割都可以通过癌症影像档案以 NIfTI 格式公开获取，网址为 https://doi.org/10.7937/tcia.2020.6c7y-gq39。还提供了详细说明与分割病例相关的临床和技术元数据的表格数据。胸腔分割对于开发病理肺部的图像分析流程非常有价值——当前的自动化算法在这方面最困难。 与“NSCLC放射组学”中已经提供的大体肿瘤体积分割相结合，胸腔积液分割对于研究积液和原发性肿瘤之间的放射组学特征差异或训练算法来区分它们可能很有价值。