Abstract:
The volume of the hippocampus is important in the progression of Alzheimer's
disease. To determine Hippocampus volume from 3D Brain MRI data, semantic
segmentation approaches have been frequently employed. This research compares
the usage of different models as backbones with U-Net architecture for semantic
segmentation of the Hippocampus from 3D Brain MRI images. The neural network
designs ResNet, SE-ResNet, ResNext, SE-ResNext, and DenseNet were employed as
the backbone for the U-Net model. For semantic segmentation of the Hippocampus,
the Decathlon 3D Brain MRI images dataset was employed. The data collection
contains 260 3D brain MRI pictures, the majority of which were created using 35
MRI slices. An exploratory data analysis was performed, which yielded helpful
insights from the dataset; the pixel intensity histogram revealed that the majority of
pixels in picture slices are bright, making the segmentation process easier for the
model. Images were preprocessed and separated into training and testing sets before
using the semantic segmentation model. Because most MRI pictures had variable
width, height, and slices, each slice of MRI image was transformed into 64x64 pixels
of width and height. The image's empty region has been assigned a 0-pixel value,
indicating that the pixel would be black in color. Data augmentation has been
performed on the preprocessed dataset to increase the samples in the dataset and to
help model generalize in a better way. As loss functions, a combination of dice loss
and categorical focused loss was utilized. With a 0.7 threshold, the IOU and F-score
metrics were utilized. The U-Net model was trained utilizing many backbones, each
of which has an influence on the model's performance and efficiency. SE-ResNet-50
performed best in terms of IOU and F-score metrics for both training and validation
sets. Other than that SE-ResNext-50 architecture also provided some good results.
