- Created by Kirk Smith, last modified by natasha honomichl on Feb 11, 2021
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Summary
Data Access
Click the Versions tab for more info about data releases.
Detailed Description
Image Statistics | |
---|---|
Modalities | Pathology |
Number of Participants | 4 |
Number of Images | 1144 |
Images Size (MB) | 196 |
Folder_Structure
- Data_Osteo_Files
- ML_Features_1144.csv - Contains 1144 rows for all the image tiles and 69 columns for filename, classification, and 65 machine learning features.
- Training_Set_1 - 11 folders with 547 images. Each folder contains 48~50 image tiles and 1 csv for annotation.
- set 1- 49 Image Tiles
- set 2- 50 Image Tiles
- set 3- 50 Image Tiles
- set 4- 50 Image Tiles
- set 5- 50 Image Tiles
- set 6- 50 Image Tiles
- set 7- 50 Image Tiles
- set 8- 50 Image Tiles
- set 9- 50 Image Tiles
- set 10- 50 Image Tiles
- set 11- 48 Image Tiles
- Training_Set_2 - 12 folders with 597 images. Each folder contains 48~50 image tiles and 1 csv for annotation.
- set 1- 49 Image Tiles
- set 2- 50 Image Tiles
- set 3- 50 Image Tiles
- set 4- 50 Image Tiles
- set 5- 50 Image Tiles
- set 6- 50 Image Tiles
- set 7- 50 Image Tiles
- set 8- 50 Image Tiles
- set 9- 50 Image Tiles
- set 10- 50 Image Tiles
- set 11- 50 Image Tiles
- set 12- 48 Image Tiles
- Training_Set_1 - 11 folders with 547 images. Each folder contains 48~50 image tiles and 1 csv for annotation.
- ML_Features_1144.csv - Contains 1144 rows for all the image tiles and 69 columns for filename, classification, and 65 machine learning features.
Citations & Data Usage Policy
Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:
Data Citation
Leavey, P., Sengupta, A., Rakheja, D., Daescu, O., Arunachalam, H. B., & Mishra, R. (2019). Osteosarcoma data from UT Southwestern/UT Dallas for Viable and Necrotic Tumor Assessment [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/tcia.2019.bvhjhdas
Publication Citation
1) Mishra, Rashika, et al. "Histopathological diagnosis for viable and non-viable tumor prediction for osteosarcoma using convolutional neural network." International Symposium on Bioinformatics Research and Applications. Springer, Cham, 2017.
2) Arunachalam, Harish Babu, et al. "Computer aided image segmentation and classification for viable and non-viable tumor identification in osteosarcoma." PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017. 2017.
3) Mishra, Rashika, et al. "Convolutional Neural Network for Histopathological Analysis of Osteosarcoma." Journal of Computational Biology 25.3 (2018): 313-325.
4) Leavey, Patrick, et al. "Implementation of Computer-Based Image Pattern Recognition Algorithms to Interpret Tumor Necrosis; a First Step in Development of a Novel Biomarker in Osteosarcoma." PEDIATRIC BLOOD & CANCER. Vol. 64. 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY, 2017.
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. DOI: 10.1007/s10278-013-9622-7
Other Publications Using This Data
TCIA maintains a list of publications which leverage TCIA data. If you have a manuscript you'd like to add please contact the TCIA Helpdesk.
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