Segmenting Soft Tissue Sarcomas

# Summary
The data is a preprocessed subset of the TCIA Study named Soft Tissue Sarcoma. The data have been converted from DICOM folders of varying resolution and data types to 3D HDF5 arrays with isotropic voxel size. This should make it easier to get started and test out various approaches (NN, RF, CRF, etc) to improve segmentations.

# TCIA Summary
This collection contains FDG-PET/CT and anatomical MR (T1-weighted, T2-weighted with fat-suppression) imaging data from 51 patients with histologically proven soft-tissue sarcomas (STSs) of the extremities. All patients had pre-treatment FDG-PET/CT and MRI scans between November 2004 and November 2011. (Note: date in the TCIA images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans). During the follow-up period, 19 patients developed lung metastases. Imaging data and lung metastases development status were used in the following study:

Vallières, M. et al. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in Medicine and Biology, 60(14), 5471-5496. doi:10.1088/0031-9155/60/14/5471.

Imaging data, tumor contours (RTstruct DICOM objects), clinical data and source code is available for this study. See the DOI below for more details and links to access the whole dataset. Please contact Martin Vallières ([e-mail]) of the Medical Physics Unit of McGill University for any scientific inquiries about this dataset.

# Acknowledgements

We would like to acknowledge the individuals and institutions that have provided data for this collection:
McGill University, Montreal, Canada - Special thanks to Martin Vallières of the Medical Physics Unit

# License
This collection is freely available to browse, download, and use for commercial, scientific and educational purposes as outlined in the Creative Commons Attribution 3.0 Unported License. See TCIA's Data Usage Policies and Restrictions for additional details. Questions may be directed to [e-mail].

# Citation
## Data Citation
Vallières, Martin, Freeman, Carolyn R., Skamene, Sonia R., & El Naqa, Issam. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2015.7GO2GSKS

## Publication Citation
Vallières, M., Freeman, C. R., Skamene, S. R., & Naqa, I. El. (2015, June 29). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Physics in Medicine and Biology. IOP Publishing. http://doi.org/10.1088/0031-9155/60/14/5471

## TCIA Citation
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)

{'# Summary': '本数据集为TCIA研究中名为软组织肉瘤的预处理子集。数据已从不同分辨率和数据类型的DICOM文件夹转换为等距体素大小的3D HDF5数组。此举旨在简化起步并测试各种方法（如神经网络、随机森林、条件随机场等）以优化分割过程。', '# TCIA Summary': '本集合收录了51名经组织学证实患有四肢软组织肉瘤（STSs）的患者FDG-PET/CT和解剖学MR（T1加权、T2加权脂肪抑制）成像数据。所有患者在2004年11月至2011年11月间接受了治疗前FDG-PET/CT和MRI扫描。（注：出于去识别化的考虑，TCIA图像中的日期已更改；同一更改适用于所有图像，以保留连续扫描之间的时间间隔）。在随访期间，19名患者发生了肺转移。影像数据和肺转移发生情况被用于以下研究：
Vallières, M. 等人（2015）。基于联合FDG-PET和MRI纹理特征构建的放射组学模型，用于预测四肢软组织肉瘤的肺转移。医学物理学杂志，60(14)，5471-5496. doi:10.1088/0031-9155/60/14/5471。
影像数据、肿瘤轮廓（RTstruct DICOM对象）、临床数据和源代码可供此研究使用。有关更多详细信息及访问整个数据集的链接，请参阅以下DOI。如对数据集的科学问题有任何疑问，请联系麦吉尔大学医学物理单元的Martin Vallières（[e-mail]）。', '# Acknowledgements': '我们感谢为该集合提供数据的个人和机构：
麦吉尔大学，加拿大蒙特利尔 - 特别感谢医学物理单元的Martin Vallières。', '# License': '本集合可免费浏览、下载和使用，用于商业、科学和教育目的，具体条款见Creative Commons Attribution 3.0 Unported License。有关TCIA的数据使用政策和限制，请参阅附加详情。有关问题，请联系[e-mail]。', '# Citation': {'## Data Citation': 'Vallières, Martin, Freeman, Carolyn R., Skamene, Sonia R., & El Naqa, Issam. (2015). 基于联合FDG-PET和MRI纹理特征构建的放射组学模型，用于预测四肢软组织肉瘤的肺转移。The Cancer Imaging Archive。http://doi.org/10.7937/K9/TCIA.2015.7GO2GSKS', '## Publication Citation': 'Vallières, M., Freeman, C. R., Skamene, S. R., & Naqa, I. El. (2015, June 29). 基于联合FDG-PET和MRI纹理特征构建的放射组学模型，用于预测四肢软组织肉瘤的肺转移。医学物理学杂志。IOP Publishing。http://doi.org/10.1088/0031-9155/60/14/5471', '## TCIA Citation': 'Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)'}}