Kernelized fuzzy c-means method in fast segmentation of demyelination plaques in multiple sclerosis

Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review

Multiple sclerosis (MS) is an autoimmune disease of the brain and spinal cord with both inflammatory and neurodegenerative features. Although advances in imaging techniques, particularly magnetic resonance imaging (MRI), have improved the process of diagnosis, its cause is unknown, a cure remains elusive and the evidence base to guide treatment is lacking. Computational techniques like machine learning (ML) have started to be used to understand MS. Published MS MRI-based computational studies can be divided into five categories: automated diagnosis; differentiation between lesion types and/or MS stages; differential diagnosis; monitoring and predicting disease progression; and synthetic MRI dataset generation. Collectively, these approaches show promise in assisting with MS diagnosis, monitoring of disease activity and prediction of future progression, all potentially contributing to disease management. Analysis quality using ML is highly dependent on the dataset size and variability used for training. Wider public access would mean larger datasets for experimentation, resulting in higher-quality analysis, permitting for more conclusive research. This narrative review provides an outline of the fundamentals of MS pathology and pathogenesis, diagnostic techniques and data types in computational analysis, as well as collating literature pertaining to the application of computational techniques to MRI towards developing a better understanding of MS.

Liver Ultrasound Image Segmentation Using Region-Difference Filters

In this paper, region-difference filters for the segmentation of liver ultrasound (US) images are proposed. Region-difference filters evaluate maximum difference of the average of two regions of the window around the center pixel. Implementing the filters on the whole image gives region-difference image. This image is then converted into binary image and morphologically operated for segmenting the desired lesion from the ultrasound image. The proposed method is compared with the maximum a posteriori-Markov random field (MAP-MRF), Chan-Vese active contour method (CV-ACM), and active contour region-scalable fitting energy (RSFE) methods. MATLAB code available online for the RSFE method is used for comparison whereas MAP-MRF and CV-ACM methods are coded in MATLAB by authors. Since no comparison is available on common database for the performance of the three methods, therefore, performance comparison of the three methods and proposed method was done on liver US images obtained from PGIMER, Chandigarh, India and from online resource. A radiologist blindly analyzed segmentation results of the 4 methods implemented on 56 images and had selected the segmentation result obtained from the proposed method as best for 46 test US images. For the remaining 10 US images, the proposed method performance was very near to the other three segmentation methods. The proposed segmentation method obtained the overall accuracy of 99.32% in comparison to the overall accuracy of 85.9, 98.71, and 68.21% obtained by MAP-MRF, CV-ACM, and RSFE methods, respectively. Computational time taken by the proposed method is 5.05 s compared to the time of 26.44, 24.82, and 28.36 s taken by MAP-MRF, CV-ACM, and RSFE methods, respectively.

Mathematical model in left ventricle segmentation

In this paper a parametric model of the left ventricle is presented. Its task is to estimate the myocardium shape on those slices, on which the segmentation algorithm has outlined the structure incorrectly. The aim of using the model on improperly segmented slices is to improve the accuracy of computing cardiac hemodynamic parameters and the heart mass. The proposed model works with any segmentation algorithm. The usefulness of the model is the largest while determining the myocardium at end-systole and calculating the heart mass. In case of the segmentation algorithm applied in this study, the error decreased from clinically unacceptable to acceptable after using the presented model.

Skeleton-based abdominal aorta registration technique

Vascular diseases are the most challenging health problems in developed countries. The vascular segmentation as well as registration techniques are the topics of past and ongoing research activities. In this work we target an abdominal aorta registration technique. The developed methodology is useful in the assessment of abdominal aortic aneurysm treatment by visualizing the correspondence between pre- and postoperative Computed Tomography (CT) data. The presented approach makes it possible to match all voxels belonging to the aorta from different CT series. It is based on aorta lumen segmentation and graph matching method. To segment the lumen area a hybrid level-set active contour approach is used. The matching step is performed based on a path similarity skeleton graph matching procedure. The registration results have been tested on the database of 8 patients, for which two different contrast-enhanced CT series were acquired. All registration results achieved with our system and verified by an expert prove the efficiency of the approach and encourage to further develop this method.

Segmentation of multiple sclerosis lesions in MR images: a review

INTRODUCTION

Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease.

METHODS

For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past.

RESULTS

This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques.

CONCLUSIONS

Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.

Segmentation of multiple sclerosis lesions in MR images: a review

INTRODUCTION

Multiple sclerosis (MS) is an inflammatory demyelinating disease that the parts of the nervous system through the lesions generated in the white matter of the brain. It brings about disabilities in different organs of the body such as eyes and muscles. Early detection of MS and estimation of its progression are critical for optimal treatment of the disease.

METHODS

For diagnosis and treatment evaluation of MS lesions, they may be detected and segmented in Magnetic Resonance Imaging (MRI) scans of the brain. However, due to the large amount of MRI data to be analyzed, manual segmentation of the lesions by clinical experts translates into a very cumbersome and time consuming task. In addition, manual segmentation is subjective and prone to human errors. Several groups have developed computerized methods to detect and segment MS lesions. These methods are not categorized and compared in the past.

RESULTS

This paper reviews and compares various MS lesion segmentation methods proposed in recent years. It covers conventional methods like multilevel thresholding and region growing, as well as more recent Bayesian methods that require parameter estimation algorithms. It also covers parameter estimation methods like expectation maximization and adaptive mixture model which are among unsupervised techniques as well as kNN and Parzen window methods that are among supervised techniques.

CONCLUSIONS

Integration of knowledge-based methods such as atlas-based approaches with Bayesian methods increases segmentation accuracy. In addition, employing intelligent classifiers like Fuzzy C-Means, Fuzzy Inference Systems, and Artificial Neural Networks reduces misclassified voxels.