Special Issue: Automated Analysis of Brain MRI using Machine Learning

Guest Editor

Jakub Nalepa, PhD, DSc

Silesian University of Technology, Poland

Future Processing Healthcare, Poland

KP Labs, Poland

Email: jnalepa@ieee.org

Manuscript Topics

Medical image analysis is undoubtedly one of the most important tasks performed by radiologists and medical physicists. Accurate delineation of abnormal tissue can be key in defining the treatment pathway, as it could be one of the factors that enables us to quantify and monitor the treatment’s efficiency and patient's response. MRI plays a key role in modern brain cancer care because it allows us to non-invasively diagnose a patient, determine the cancer stage, monitor the treatment, assess, and quantify its results, and understand its potential side effects. MRI may be exploited to better understand both structural and functional characteristics of the tissue – such detailed and clinically-relevant analysis of an imaged tumor can help design more personalized treatment pathways, and ultimately lead to a better patient care. Additionally, MRI does not use the damaging ionizing radiation, and may be utilized to acquire images in different planes and orientations. Thus, MRI is the investigative tool of choice for brain tumors.

The aim of this Special Issue is to present recent advances in automated analysis of brain MRI using machine learning (especially deep learning) and advanced data analysis. As manual delineation and further investigation of such imagery is user-dependent, time-consuming and difficult to reproduce due to the inter- and intra-rater variability, developing automated tools that can accelerate the analysis and make it fully reproducible are pivotal to bring such techniques into clinical practice.

The following topics that span through the entire MRI processing chain are of interest in this Special Issue (note that this list is not exhaustive):
• Brain extraction from MRI.
• Brain tumor detection, segmentation and classification from MRI.
• Feature extraction from multi-modal MRI.
• Deep learning in brain tumor analysis.
• Data pre-processing and augmentation for brain tumor analysis using deep learning.
• Radiomics in brain tumor imaging.
• Tumor grading and extraction of quantifiable tumor characteristics.
• Automated analysis of functional brain imaging.
• Pre- and post-operative brain imaging.
• Prediction of tumor progression using machine learning.
• Verification and validation of brain image analysis software in clinical settings.
• Certification of machine learning-powered software tools for brain tumor analysis.

Instructions for authors
Please submit your manuscript to online submission system

Paper Submission

All manuscripts will be peer-reviewed before their acceptance for publication. The deadline for manuscript submission is 16 December 2021

Published Papers({{count}})

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