A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Active Learning on Medical Image

The development of medical science greatly depends on the increased utilization of machine learning algorithms. By incorporating machine learning, the medical imaging field can significantly improve in terms of the speed and accuracy of the diagnostic process. Computed tomography (CT), magnetic resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron emission tomography (PET) are the most commonly used types of imaging data in the diagnosis process, and machine learning can aid in detecting diseases at an early stage. However, training machine learning models with limited annotated medical image data poses a challenge. The majority of medical image datasets have limited data, which can impede the pattern-learning process of machine-learning algorithms. Additionally, the lack of labeled data is another critical issue for machine learning. In this context, active learning techniques can be employed to address the challenge of limited annotated medical image data. Active learning involves iteratively selecting the most informative samples from a large pool of unlabeled data for annotation by experts. By actively selecting the most relevant and informative samples, active learning reduces the reliance on large amounts of labeled data and maximizes the model's learning capacity with minimal human labeling effort. By incorporating active learning into the training process, medical imaging machine learning models can make more efficient use of the available labeled data, improving their accuracy and performance. This approach allows medical professionals to focus their efforts on annotating the most critical cases, while the machine learning model actively learns from these annotated samples to improve its diagnostic capabilities.Comment: 12 pages, 8 figures; Acceptance of the chapter for the Springer book "Data-driven approaches to medical imaging

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.This research was funded by Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-02G31, and the Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research. The work and the contribution were also supported by the project Smart Solutions in Ubiquitous Computing Environments, Grant Agency of Excellence, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic (under ID: UHK-FIM-GE-2018). Furthermore, the research is also partially supported by the Spanish Ministry of Science, Innovation and Universities with FEDER funds in the project TIN2016-75850-R

Machine Learning in Medical Image Analysis

Machine learning is playing a pivotal role in medical image analysis. Many algorithms based on machine learning have been applied in medical imaging to solve classification, detection, and segmentation problems. Particularly, with the wide application of deep learning approaches, the performance of medical image analysis has been significantly improved. In this thesis, we investigate machine learning methods for two key challenges in medical image analysis: The first one is segmentation of medical images. The second one is learning with weak supervision in the context of medical imaging. The first main contribution of the thesis is a series of novel approaches for image segmentation. First, we propose a framework based on multi-scale image patches and random forests to segment small vessel disease (SVD) lesions on computed tomography (CT) images. This framework is validated in terms of spatial similarity, estimated lesion volumes, visual score ratings and was compared with human experts. The results showed that the proposed framework performs as well as human experts. Second, we propose a generic convolutional neural network (CNN) architecture called the DRINet for medical image segmentation. The DRINet approach is robust in three different types of segmentation tasks, which are multi-class cerebrospinal fluid (CSF) segmentation on brain CT images, multi-organ segmentation on abdomen CT images, and multi-class tumour segmentation on brain magnetic resonance (MR) images. Finally, we propose a CNN-based framework to segment acute ischemic lesions on diffusion weighted (DW)-MR images, where the lesions are highly variable in terms of position, shape, and size. Promising results were achieved on a large clinical dataset. The second main contribution of the thesis is two novel strategies for learning with weak supervision. First, we propose a novel strategy called context restoration to make use of the images without annotations. The context restoration strategy is a proxy learning process based on the CNN, which extracts semantic features from images without using annotations. It was validated on classification, localization, and segmentation problems and was superior to existing strategies. Second, we propose a patch-based framework using multi-instance learning to distinguish normal and abnormal SVD on CT images, where there are only coarse-grained labels available. Our framework was observed to work better than classic methods and clinical practice.Open Acces

Machine Learning in Chronic Pain Research: A Scoping Review

Given the high prevalence and associated cost of chronic pain, it has a significant impact on individuals and society. Improvements in the treatment and management of chronic pain may increase patients’ quality of life and reduce societal costs. In this paper, we evaluate state-of-the-art machine learning approaches in chronic pain research. A literature search was conducted using the PubMed, IEEE Xplore, and the Association of Computing Machinery (ACM) Digital Library databases. Relevant studies were identified by screening titles and abstracts for keywords related to chronic pain and machine learning, followed by analysing full texts. Two hundred and eighty-seven publications were identified in the literature search. In total, fifty-three papers on chronic pain research and machine learning were reviewed. The review showed that while many studies have emphasised machine learning-based classification for the diagnosis of chronic pain, far less attention has been paid to the treatment and management of chronic pain. More research is needed on machine learning approaches to the treatment, rehabilitation, and self-management of chronic pain. As with other chronic conditions, patient involvement and self-management are crucial. In order to achieve this, patients with chronic pain need digital tools that can help them make decisions about their own treatment and care