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Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.

Publication ,  Journal Article
AlBadawy, EA; Saha, A; Mazurowski, MA
Published in: Med Phys
March 2018

BACKGROUND AND PURPOSE: Convolutional neural networks (CNNs) are commonly used for segmentation of brain tumors. In this work, we assess the effect of cross-institutional training on the performance of CNNs. METHODS: We selected 44 glioblastoma (GBM) patients from two institutions in The Cancer Imaging Archive dataset. The images were manually annotated by outlining each tumor component to form ground truth. To automatically segment the tumors in each patient, we trained three CNNs: (a) one using data for patients from the same institution as the test data, (b) one using data for the patients from the other institution and (c) one using data for the patients from both of the institutions. The performance of the trained models was evaluated using Dice similarity coefficients as well as Average Hausdorff Distance between the ground truth and automatic segmentations. The 10-fold cross-validation scheme was used to compare the performance of different approaches. RESULTS: Performance of the model significantly decreased (P < 0.0001) when it was trained on data from a different institution (dice coefficients: 0.68 ± 0.19 and 0.59 ± 0.19) as compared to training with data from the same institution (dice coefficients: 0.72 ± 0.17 and 0.76 ± 0.12). This trend persisted for segmentation of the entire tumor as well as its individual components. CONCLUSIONS: There is a very strong effect of selecting data for training on performance of CNNs in a multi-institutional setting. Determination of the reasons behind this effect requires additional comprehensive investigation.

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Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

March 2018

Volume

45

Issue

3

Start / End Page

1150 / 1158

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging
  • Machine Learning
  • Image Processing, Computer-Assisted
  • Humans
  • Brain Neoplasms
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
 

Citation

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AlBadawy, E. A., Saha, A., & Mazurowski, M. A. (2018). Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys, 45(3), 1150–1158. https://doi.org/10.1002/mp.12752
AlBadawy, Ehab A., Ashirbani Saha, and Maciej A. Mazurowski. “Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.Med Phys 45, no. 3 (March 2018): 1150–58. https://doi.org/10.1002/mp.12752.
AlBadawy EA, Saha A, Mazurowski MA. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys. 2018 Mar;45(3):1150–8.
AlBadawy, Ehab A., et al. “Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing.Med Phys, vol. 45, no. 3, Mar. 2018, pp. 1150–58. Pubmed, doi:10.1002/mp.12752.
AlBadawy EA, Saha A, Mazurowski MA. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med Phys. 2018 Mar;45(3):1150–1158.

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

March 2018

Volume

45

Issue

3

Start / End Page

1150 / 1158

Location

United States

Related Subject Headings

  • Nuclear Medicine & Medical Imaging
  • Neural Networks, Computer
  • Magnetic Resonance Imaging
  • Machine Learning
  • Image Processing, Computer-Assisted
  • Humans
  • Brain Neoplasms
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis