Convolutional neural networks for brain tumour segmentation.

Area of Science:

• Medical imaging analysis
• Machine learning in oncology
• Neuro-oncology

Background:

• Quantitative image analysis, including radiomics, aids in predicting clinical outcomes.
• Brain tumors, particularly glioblastoma multiforme (GBM), are a significant area of interest for image analysis.
• Manual tumor segmentation is inconsistent, necessitating automated solutions.

Purpose of the Study:

• To investigate the role of convolutional neural networks (CNNs) in segmenting brain tumors.
• To provide an educational overview of CNNs and establish an example segmentation pipeline.
• To explore the future application of CNNs in radiomics for predicting clinical outcomes in GBM.

Main Methods:

• Literature search to identify CNN methodologies for tumor segmentation.
• Educational review of CNN architecture and function.
• Exploration of radiomics features (shape, texture, signal intensity) for outcome prediction.

Main Results:

• CNNs show promise for automated and consistent brain tumor segmentation.
• Radiomics analysis of quantitative features can potentially predict clinical outcomes.
• A framework for CNN-based segmentation and radiomics analysis is outlined.

Conclusions:

• Automated segmentation using CNNs addresses the limitations of manual methods in brain tumor analysis.
• The integration of CNNs and radiomics offers a powerful approach for predicting GBM patient outcomes.
• Further research into CNNs and radiomics can enhance personalized treatment strategies for brain tumors.