Semantic segmentation for brain tumor analysis is a crucial application within the realm of computer vision, enabling early and precise diagnoses with heightened accuracy and safety. This advanced technique involves pixel-level classification, where each pixel in an image is assigned a label representing its corresponding object or feature. Particularly in tumor detection, semantic segmentation plays a pivotal role in identifying and distinguishing tumors from neighboring regions within the brain image, such as healthy tissue, edema, and necrotic regions. By harnessing deep learning architectures like U-NET and capitalizing on annotated datasets, semantic segmentation models become adept at recognizing tumors and precisely outlining their boundaries. This innovation holds transformative potential for revolutionizing the biomedical imaging domain, leading to more dependable and secure early patient diagnoses. The capacity to comprehend intricate brain tumor details on a micro level empowers healthcare systems with the acumen required for instantaneous decision-making, thereby ensuring patient well-being and fostering safer and more efficient patient care and diagnostics. This advancement is poised to significantly enhance patient care and diagnosis through a more nuanced understanding of brain tumor characteristics and their intricate interactions with surrounding anatomy, ultimately resulting in informed decision-making and tailored treatment strategies.

Source: https://www.kaggle.com/datasets/awsaf49/brats20-dataset-training-validation

BraTS (Multimodal Brain Tumor Segmentation) dataset is a widely used collection of medical imaging data obtained through MRI scans for brain imaging, each represented as NIfTI files (.nii.gz). These files are a common format for storing medical imaging data.

MRI Modalities Included:

1. T1 (T1-weighted):

    • Image Type: Native image.
    • Acquisition: Sagittal or axial 2D acquisitions.
    • Slice Thickness: 1–6 mm.
   

2. T1c (T1-weighted with Contrast Enhancement):

    • Image Type: Contrast-enhanced (Gadolinium) image.
    • Acquisition: 3D acquisition.
    • Voxel Size: 1 mm isotropic voxel size for most patients.
   

3. T2 (T2-weighted):

    • Image Type: T2-weighted image.
    • Acquisition: Axial 2D acquisition.
    • Slice Thickness: 2–6 mm.
   

4. FLAIR (T2-weighted FLAIR):

    • Image Type: T2-weighted FLAIR image.
    • Acquisition: Axial, coronal, or sagittal 2D acquisitions.
    • Slice Thickness: 2–6 mm
   

The U-Net model is a deep convolutional neural network architecture created specifically for semantic segmentation tasks, where the objective is to segment and identify each pixel in an image with an appropriate class, particularly in tasks like glioma segmentation in pre-operative MRI scans. It is known for its effectiveness in capturing fine-grained details while maintaining context, making it suitable for intricate segmentation tasks

  Fig: U-Net Model Architecture 

The main elements of the U-Net architecture are as follows:

The input image is processed by the U-Net encoder portion using a number of convolutional and pooling layers. The encoder is in charge of recognising significant patterns and traits that 8 will enable accurate segmentation and collecting contextual information from the input image. Two 3x3 convolutions are applied repeatedly in the encoder, and after each one, a rectified linear unit (ReLU) and a 2x2 max pooling operation with stride 2 are applied for downsampling. The encoder can extract more precise information by mixing various convolutional and pooling layers. While the depth steadily increases, the size of the image gradually decreases. The encoder is simply a conventional stack of max pooling and convolutional layers. Downsampling and feature extraction are performed by the encoder block in the U-Net architecture.

  Fig: Encoder in U-Net Model

Upsampling the features to the original image resolution is done by the decoder in the U-Net architecture. It accomplishes this using other convolutional layers and transposed convolutions, also referred to as deconvolutions. The decoder's task is to piece together the segmented output using the features that the encoder extracted. The same connectivity is created by the transposed convolution procedure but in the other direction from the standard convolution. A low-resolution image is present in the input volume, and a high-resolution image is present in the output volume. Upsampling, concatenation, and standard convolution operations make up the decoder. By upsampling the feature maps to the original picture resolution, transposed convolutions are used in the decoder to improve the spatial resolution of the feature maps.

  Fig: Decoder in U-Net Model

  Fig: Sample Input

  Fig: Output