A deep learning algorithm for white matter hyperintensity lesion detection and segmentation

Purpose: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. / Methods: We developed and evaluated “DeepWML”, a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). / Results: The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool’s performance increased with larger lesion volumes. / Conclusion: DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation

A patch-based convolutional neural network for localized MRI brain segmentation.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Accurate segmentation of the brain is an important prerequisite for effective diagnosis, treatment planning, and patient monitoring. The use of manual Magnetic Resonance Imaging (MRI) segmentation in treating brain medical conditions is slowly being phased out in favour of fully-automated and semi-automated segmentation algorithms, which are more efficient and objective. Manual segmentation has, however, remained the gold standard for supervised training in image segmentation. The advent of deep learning ushered in a new era in image segmentation, object detection, and image classification. The convolutional neural network has contributed the most to the success of deep learning models. Also, the availability of increased training data when using Patch Based Segmentation (PBS) has facilitated improved neural network performance. On the other hand, even though deep learning models have achieved successful results, they still suffer from over-segmentation and under-segmentation due to several reasons, including visually unclear object boundaries. Even though there have been significant improvements, there is still room for better results as all proposed algorithms still fall short of 100% accuracy rate. In the present study, experiments were carried out to improve the performance of neural network models used in previous studies. The revised algorithm was then used for segmenting the brain into three regions of interest: White Matter (WM), Grey Matter (GM), and Cerebrospinal Fluid (CSF). Particular emphasis was placed on localized component-based segmentation because both disease diagnosis and treatment planning require localized information, and there is a need to improve the local segmentation results, especially for small components. In the evaluation of the segmentation results, several metrics indicated the effectiveness of the localized approach. The localized segmentation resulted in the accuracy, recall, precision, null-error, false-positive rate, true-positive and F1- score increasing by 1.08%, 2.52%, 5.43%, 16.79%, -8.94%, 8.94%, 3.39% respectively. Also, when the algorithm was compared against state of the art algorithms, the proposed algorithm had an average predictive accuracy of 94.56% while the next best algorithm had an accuracy of 90.83%

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies