yahyaalaamassoud/brain-tumor-segmentation

Exploration of tumor segmentation from MRI scans, using thresholding and region growing methods.

Brain Tumor Segmentation from MRI Scans Paper

This paper presents an exploration of tumor segmentation from MRI scans, focusing on the efficacy of thresholding and region growing methods, using manual and automatic seed selection. We employed two diverse datasets from the Brain Tumor Segmentation (BraTS) challenge to evaluate our approaches. We meticulously compare the performance of our methods against eight established metrics, encompassing both quantitative and qualitative dimensions. Quantitative analysis involves metrics such as Jaccard similarity and dice coefficient, accuracy, specificity, and runtime, providing a multi-faceted view of the segmentation performance. Qualitative analysis is conducted through visual inspection, ensuring that the segmented tumors align closely with the groundtruth labels. The results demonstrate a superiority of the region growing methods, especially with automatic seed selection, in accurately delineating tumor boundaries. This study contributes significantly to the field of medical image processing, offering insights that could be useful for tumor segmentation.

This project has been conducted as a part of my PhD at the University of Ottawa, Canada.

brain-tumor-segmentation's Issues

Visualize raw MRI scans in 3D and 2D, as well as the segmentation masks

Create a Jupyter notebook to read the raw MRI scans.

Prepare the data in two subfolders (set_1 and set_2). set_1 contains 12 images and set_2 contains 15 images

Create a Jupyter notebook for evaluation metrics for segmentation algorithms