Segmentation of Brain MRI images using Deep Learning Techniques.

http://braintumorsegmentation.org/

4D array of MR images in the shape of (240, 240, 155, 4). The first 3 dimensions are the X, Y, and Z values for each point in the 3D volume, which is commonly called a voxel. The 4th dimension is the values for 4 different sequences 0: FLAIR: "Fluid Attenuated Inversion Recovery" (FLAIR) 1: T1 2: T1c 3: T2

A 3D array with the shape of (240, 240, 155). The integer values in this array indicate the "label" for each voxel in the corresponding image files: 0: Background 1: Edema 2: Non-enhancing tumor 3: Enhancing tumor Edema ,Non-enhancing tumour , Enhancing tumour are constituents of Glioma(Brain Tumour).

[1] Exploring and Preprocessing of Data

[2] Making CNN(U-Net) Model

[3] Metrics

[1] https://github.com/wiqaaas/youtube/tree/master/Deep_Learning_Using_Tensorflow/Image_Segmentation_using_U-Net

[2] https://www.analyticsvidhya.com/blog/2021/06/introduction-to-skull-stripping-image-segmentation-on-3d-mri-images/#h2_6

[3] https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwi91fbsrJXxAhVeH7cAHSZjC5AQFjAGegQIChAE&url=https%3A%2F%2Fwww.mdpi.com%2F2076-3417%2F9%2F3%2F569%2Fpdf-vor&usg=AOvVaw27QvVMiUr0X3aU3ytxpzHX

[4]https://arxiv.org/pdf/1810.10853.pdf

[5] https://link.springer.com/chapter/10.1007/978-3-030-11726-9_4