Automatic Brain Tumor Segmentation with Scale Attention Network
- URL: http://arxiv.org/abs/2011.03188v3
- Date: Wed, 25 Nov 2020 19:16:09 GMT
- Title: Automatic Brain Tumor Segmentation with Scale Attention Network
- Authors: Yading Yuan
- Abstract summary: Multimodal Brain Tumor Challenge 2020 (BraTS 2020) provides a common platform for comparing different automatic algorithms on multi-parametric Magnetic Resonance Imaging (mpMRI)
We propose a dynamic scale attention mechanism that incorporates low-level details with high-level semantics from feature maps at different scales.
Our framework was trained using the 369 challenge training cases provided by BraTS 2020, and achieved an average Dice Similarity Coefficient (DSC) of 0.8828, 0.8433 and 0.8177, as well as 95% Hausdorff distance (in millimeter) of 5.2176, 17.9697 and 13.4298 on 166 testing cases for whole tumor
- Score: 1.7767466724342065
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Automatic segmentation of brain tumors is an essential but challenging step
for extracting quantitative imaging biomarkers for accurate tumor detection,
diagnosis, prognosis, treatment planning and assessment. Multimodal Brain Tumor
Segmentation Challenge 2020 (BraTS 2020) provides a common platform for
comparing different automatic algorithms on multi-parametric Magnetic Resonance
Imaging (mpMRI) in tasks of 1) Brain tumor segmentation MRI scans; 2)
Prediction of patient overall survival (OS) from pre-operative MRI scans; 3)
Distinction of true tumor recurrence from treatment related effects and 4)
Evaluation of uncertainty measures in segmentation. We participate the image
segmentation challenge by developing a fully automatic segmentation network
based on encoder-decoder architecture. In order to better integrate information
across different scales, we propose a dynamic scale attention mechanism that
incorporates low-level details with high-level semantics from feature maps at
different scales. Our framework was trained using the 369 challenge training
cases provided by BraTS 2020, and achieved an average Dice Similarity
Coefficient (DSC) of 0.8828, 0.8433 and 0.8177, as well as 95% Hausdorff
distance (in millimeter) of 5.2176, 17.9697 and 13.4298 on 166 testing cases
for whole tumor, tumor core and enhanced tumor, respectively, which ranked
itself as the 3rd place among 693 registrations in the BraTS 2020 challenge.
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