Brain Tumour 3D Segmentation

Medical image segmentation is a crucial task in medical imaging analysis, as it plays an important role in diagnosis, treatment planning, and disease monitoring. Accurate segmentation is particularly crucial in the case of cancer, where the precise identification and delineation of tumours is critical in aiding treatment planning and monitoring.

Traditionally, medical image analysis has been performed manually by specialists, which is time-consuming and prone to bias and inconsistent analysis. This has led to an increased interest in developing automated image segmentation algorithms based on machine learning and deep learning techniques. With the advancements in these techniques, the field has witnessed tremendous progress in recent years, with deep learning-based segmentation models achieving state-of-the-art results that are close to human accuracy.

Introduction

The MONAI (Medical Open Network for AI) library is an open-source framework for developing deep learning models in medical imaging. It provides a range of pre-built models and tools for developing custom models for medical image analysis. The MONAI library includes various algorithms for medical image segmentation, such as 2D and 3D U-Net, 3D SegResNet, and 3D FPN.

In this project, we developed a 3D segmentation model for brain tumours using the MONAI 3D SegResNet algorithm. Accurate brain tumour segmentation is of paramount importance in medical practice, as it plays a vital role in determining the extent and aggressiveness of cancers.

The MONAI 3D SegResNet algorithm is a widely used deep learning-based segmentation algorithm, which has shown promising results in various medical image segmentation tasks. This algorithm consists of a series of residual blocks, which learn to capture increasingly complex image features.

We chose this algorithm due to its effectiveness and the availability of open-source implementations. We have evaluated the performance of our models based on the Dice similarity score, which is a widely used metric for segmentation tasks.

Objectives

The objective of this experiment was to develop a 3D segmentation model using the MONAI algorithm for the accurate segmentation of tumours, organs, and tissues from medical images.

Applications of 3D Segmentation Modelling

Accurate segmentation of medical images can improve the diagnosis, treatment planning, and monitoring of various diseases. Specifically with regards to the segmentation of brain tumours, it is essential for effective treatment planning and monitoring.

Machine learning models, such as the MONAI 3D SegResNet algorithm, have shown great potential in accurately and efficiently segmenting medical images.

The use of such models can lead to significant improvements in treatment outcomes by providing clinicians with more precise and detailed information about the location and shape of tumours. This, in turn, can aid in the selection of appropriate treatment options and help in monitoring the progression of the disease.

Moreover, the use of machine learning models can help reduce the time and effort required for the manual segmentation of medical images. This can lead to more efficient and cost-effective diagnosis and treatment planning.

Overall, the accurate segmentation of medical images using machine learning models has the potential to revolutionise the field of medical imaging and improve patient outcomes.