Segmentation of Brain Tumours through Volumetric Analysis Utilizing U-Net on MRI Brain Images
TL;DR
The 3D U-Net will improve volumetric analysis quality and speed by delivering valid data on the tumour size and morphology which is the key aspect requiring improvement in terms of clinical decision-making.
Abstract
The 3D U-Net structure has emerged as a first priority in segmenting of brain tumours especially defining regions of interests (ROIs) to measure volumes. This deep learning model has been specifically optimized to take in three-dimensional data, and thus it can be applied effectively to medical imaging work where spatial context is essential. Its symmetric encoder-decoder structure enables the extraction of both high-level semantic features and fine-grained spatial information. The 3D U-Net requires annotated volumetric data-sets, typically MRI data-sets, during training period to know how to differentiate between tumour and normal brain tissue. Through voxel-based classification, tumour boundary is accurately delineated, which helps to easily identify the ROI to perform further clinical assessment.In addition to segmentation, the 3D U-Net will improve volumetric analysis quality and speed by delivering valid data on the tumour size and morphology which is the key aspect requiring improvement in terms of clinical decision-making. The skip connections between the encoder and the decoder pathway contribute towards the maintenance of spatial resolution which is a prevailing drawback of traditional convolutional networks, and enhance the overall segmentation accuracy. This aspect of providing a consistent and high-resolution understanding of the tumour characteristics in order to enable clinicians to customize their treatment strategy into better patient care and outcomes is made possible by the 3D U-Net.