FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial
Intelligence Developed for Brain
- URL: http://arxiv.org/abs/2208.14360v1
- Date: Tue, 30 Aug 2022 16:06:07 GMT
- Title: FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial
Intelligence Developed for Brain
- Authors: Mostafa Mehdipour Ghazi and Mads Nielsen
- Abstract summary: A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions.
The proposed model uses an efficient U-Net-like network and benefits from the intersection points of different views and hierarchical relations.
The proposed method can be applied to brain MRI data including skull or any other artifacts without preprocessing the images or a drop in performance.
- Score: 0.8376091455761259
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Medical images used in clinical practice are heterogeneous and not the same
quality as scans studied in academic research. Preprocessing breaks down in
extreme cases when anatomy, artifacts, or imaging parameters are unusual or
protocols are different. Methods robust to these variations are most needed. A
novel deep learning method is proposed for fast and accurate segmentation of
the human brain into 132 regions. The proposed model uses an efficient
U-Net-like network and benefits from the intersection points of different views
and hierarchical relations for the fusion of the orthogonal 2D planes and brain
labels during the end-to-end training. Weakly supervised learning is deployed
to take the advantage of partially labeled data for the whole brain
segmentation and estimation of the intracranial volume (ICV). Moreover, data
augmentation is used to expand the magnetic resonance imaging (MRI) data by
generating realistic brain scans with high variability for robust training of
the model while preserving data privacy. The proposed method can be applied to
brain MRI data including skull or any other artifacts without preprocessing the
images or a drop in performance. Several experiments using different atlases
are conducted to evaluate the segmentation performance of the trained model
compared to the state-of-the-art, and the results show higher segmentation
accuracy and robustness of the proposed model compared to the existing methods
across different intra- and inter-domain datasets.
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