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Hippocampus segmentation in MR brain images using learned fuzzy mask and U-Net
Poster Presentation XML
Authors
1Department of Computer, Engineering, Bu Ali Sina University, Hamedan, Iran (08134291310)
2Department of Computer Engineering, Bu Ali Sina University, Hamedan, Iran
Abstract
The hippocampus is an important part of the human brain that is damaged in some diseases such as Alzheimer's, schizophrenia, and epilepsy. This paper presents a new method in hippocampus segmentation which is applicable in the early diagnosis of mentioned diseases. This method has introduced a two-section model to detect the hippocampus region in brain MR images. In the first section, the location of the hippocampus is roughly detected using a U-Net neural network model, and then a fuzzy mask is created around the detected area using a fuzzy function. In the second section, this mask is applied to the brain images and a U-Net neural network is used to segment these masked images, which finally predicts the location of the hippocampus. The main advantage and idea of this method is the use of a pre-trained fuzzy mask, which increases the quality of segmentation. The proposed method in this research was trained and tested using the HARP dataset, which contains 135 T1-weighted MRI volumes and the proposed model reached 0.95 dice in the best case.
Keywords
 
Proceeding Title [Persian]
Hippocampus segmentation in MR brain images using learned fuzzy mask and U-Net
Authors [Persian]
Abstract [Persian]
The hippocampus is an important part of the human brain that is damaged in some diseases such as Alzheimer's, schizophrenia, and epilepsy. This paper presents a new method in hippocampus segmentation which is applicable in the early diagnosis of mentioned diseases. This method has introduced a two-section model to detect the hippocampus region in brain MR images. In the first section, the location of the hippocampus is roughly detected using a U-Net neural network model, and then a fuzzy mask is created around the detected area using a fuzzy function. In the second section, this mask is applied to the brain images and a U-Net neural network is used to segment these masked images, which finally predicts the location of the hippocampus. The main advantage and idea of this method is the use of a pre-trained fuzzy mask, which increases the quality of segmentation. The proposed method in this research was trained and tested using the HARP dataset, which contains 135 T1-weighted MRI volumes and the proposed model reached 0.95 dice in the best case.
Keywords [Persian]
Hippocampus، hippocampus segmentation، fuzzy mask