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A Fully Automated Deep Learning Network for Brain Tumor Segmentation. | LitMetric

AI Article Synopsis

  • A new automated method for brain tumor segmentation was developed using deep learning, based on 285 brain tumor cases from the BraTS2018 dataset and employing 3D-Dense-UNets for better accuracy.
  • The method achieved high mean Dice-scores for segmenting whole tumors (0.92), tumor cores (0.84), and enhancing tumors (0.80) using cross-validation.
  • Testing on additional datasets showed consistent high accuracy, indicating the method's potential for future clinical application.

Article Abstract

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the complex multiclass segmentation problem into individual binary-segmentation problems for each subcomponent. We implemented a 3-fold cross-validation to generalize the network's performance. The mean cross-validation Dice-scores for whole tumor (WT), tumor core (TC), and enhancing tumor (ET) segmentations were 0.92, 0.84, and 0.80, respectively. We then retrained the individual binary-segmentation networks using 265 of the 285 cases, with 20 cases held-out for testing. We also tested the network on 46 cases from the BraTS2017 validation data set, 66 cases from the BraTS2018 validation data set, and 52 cases from an independent clinical data set. The average Dice-scores for WT, TC, and ET were 0.90, 0.84, and 0.80, respectively, on the 20 held-out testing cases. The average Dice-scores for WT, TC, and ET on the BraTS2017 validation data set, the BraTS2018 validation data set, and the clinical data set were as follows: 0.90, 0.80, and 0.78; 0.90, 0.82, and 0.80; and 0.85, 0.80, and 0.77, respectively. A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy on the BraTS data set and on the independent clinical data set. This method is promising for implementation into a clinical workflow.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289260PMC
http://dx.doi.org/10.18383/j.tom.2019.00026DOI Listing

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