  In recent years, Deep Learning (DL) has shown promising results in conducting
AI tasks such as computer vision and image segmentation. Specifically,
Convolutional Neural Network (CNN) models in DL have been applied to
prevention,detection, and diagnosis in predictive medicine. Image segmentation
plays a significant role in disease detection and prevention.However, there are
enormous challenges in performing DL-based automatic segmentation due to the
nature of medical images such as heterogeneous modalities and formats,
insufficient labeled training data, and the high-class imbalance in the labeled
data. Furthermore, automating segmentation of medical images,like magnetic
resonance images (MRI), becomes a challenging task. The need for automated
segmentation or annotation is what motivates our work. In this paper, we
propose a fully automated approach that aims to segment the human claustrum for
analytical purposes. We applied a U-Net CNN model to segment the claustrum (Cl)
from a MRI dataset. With this approach, we have achieved an average Dice per
case score of 0.72 for Cl segmentation, with K=5 for cross-validation. The
expert in the medical domain also evaluates these results.
