Magnetic resonance image-based brain tumour segmentation

Abstract

Brain tumour segmentation using MRI images is an important task in clinical practice, as it can provide crucial information for treatment planning and monitoring. The U-Net architecture has emerged as a popular neural network model for medical image segmentation, including brain tumour segmentation in MRI images. U-Net is a fully convolutional neural network that combines an encoder and decoder structure with skip connections to enable accurate and efficient segmentation of medical images. The U-Net architecture has several advantages for brain tumour segmentation in MRI images. It can capture complex spatial relationships between different brain structures, while also being able to handle the variability and noise present in medical imaging data. Additionally, skip connections allow the network to propagate detailed spatial information from the encoder to the decoder, which can improve the segmentation accuracy, especially for small or complex structures. Numerous studies have demonstrated the effectiveness of U-Net for brain tumour segmentation in MRI images, achieving state-of-the-art results in many cases. U Net has been used to segment different types of brain tumours, including gliomas, meningiomas, and metastases, with high accuracy and consistency. U-Net has also been shown to perform well on multi-modal MRI images, which can provide more detailed and complementary information for tumour segmentation. In conclusion, U-Net is a powerful and effective architecture for brain tumour segmentation in MRI images, offering high accuracy and efficiency in many cases. However, the limitations of U-Net should also be carefully considered when developing and evaluating segmentation models for clinical practice.