Brain tumors, growths of cells in or near the brain, present significant challenges for diagnosis and treatment. MRI images are a common imaging method for evaluating these tumors, but the volume of data generated poses difficulties for manual segmentation, limiting precise quantitative assessments in clinical settings. Automatic segmentation methods are essential to address this challenge. This project focuses on developing deep learning models based on convolutional neural networks (CNNs) and watershed algorithms for automated semantic image segmentation of MRI brain images.

The spatial and structural heterogeneity among brain tumors makes automatic segmentation a challenging task. Dependable and automatic segmentation methods are needed to facilitate accurate diagnosis and treatment planning.

The BraTS (Brain Tumor Segmentation) dataset is utilized for training and evaluation. This dataset consists of MRI images of the brain, annotated to indicate tumor regions.

We employ CNNs and watershed algorithms for automated semantic image segmentation of brain MRI images. Various CNN architectures are explored and evaluated on the BraTS dataset. Different regularization methods and hyperparameters are tested and optimized through a series of experiments to enhance segmentation performance.

The developed deep learning models achieve promising results in segmenting brain tumors from MRI images. Performance metrics such as Dice coefficient, sensitivity, and specificity are used to assess the accuracy and reliability of the segmentation results.

A user-friendly web application is created to enable medical practitioners to utilize the developed models easily. The application provides an intuitive interface for uploading MRI images and obtaining segmented results.

This project demonstrates the efficacy of deep learning models for automated semantic image segmentation of brain MRI images. By leveraging CNNs and watershed algorithms, we achieve accurate and reliable segmentation, facilitating improved diagnosis and treatment planning for brain tumors. The development of a web application enhances the accessibility and usability of the segmentation models in clinical practice.