USE OF U-NET WITH DIFFERENT DEPTHS FOR MENINGIOMA, GLIOMA AND PITUITARY TUMOR SEGMENTATION
Brain tumor is an abnormal cell in brain organ that can develop over time. Segmentation can be used to help monitor tumor development also its location. Deep learning can be used to do segmentation of brain tumor. U-Net is one of the deep learning architecture that can be used for segmentating br...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/79998 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Brain tumor is an abnormal cell in brain organ that can develop over time.
Segmentation can be used to help monitor tumor development also its location.
Deep learning can be used to do segmentation of brain tumor. U-Net is one of the
deep learning architecture that can be used for segmentating brain tumor. U-Net’s
depth can be modified. U-Net’s depth can affect segmentation performance. Deeper
U-Net performs higher F1-score but no significant improvement in the last three
levels. Improvement occurs from level 1 to level 2 about 17.9%, level 2 to level 3
about 3.6%, level 3 to level 4 about 0.7%, and level 4 to level 5 about 1.1%. For
meningioma, intrinsic factors affecting segmentation performance include
circularity and mean intensity. In pituitary tumors, the features affecting
segmentation performance are standard deviation of intensity. In glioma, the
features affecting segmentation performance include circularity, standard deviation
of intensity, and maximum intensity. U-Net’s failure to segmentate images that has
inconsistency of ground truth, tumor intensity characteristics, quality of image
acquisition. U-Net also can not learn the symmetry of brain to help segmentation
and distinguish pituitary tumor and its surrounding. |
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