Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data

LMFE: Learning-Based Multiscale Feature Engineering in Partial Discharge Detection

The partial discharge (PD) detection is of critical importance in the stability and continuity of power distribution operations. Although several feature engineering methods have been developed to refine and improve PD detection accuracy, they can be suboptimal due to several major issues: 1) failure in identifying fault-related pulses; 2) the lack of inner-phase temporal representation; and 3) multiscale feature integration. The aim of this article is to develop a learning-based multiscale feature engineering (LMFE) framework for PD detection of each signal in a three-phase power system, while addressing the above issues. The three-phase measurements are first preprocessed to identify the pulses together with the surrounded waveforms. Next, our feature engineering is conducted to extract the global-scale features, i.e., phase-level and measurement-level aggregations of the pulse-level information, and the local-scale features focusing on waveforms and their inner-phase temporal information. A recurrent neural network (RNN) model is trained, and intermediate features are extracted from this trained RNN model. Furthermore, these multiscale features are merged and fed into a classifier to distinguish the different patterns between faulty and nonfaulty signals. Finally, our LMFE is evaluated by analyzing the VSB ENET dataset, which shows that LMFE outperforms existing approaches and provides the state-of-the-art solution in PD detection.

A Comprehensive Review on MRI-based Knee Joint Segmentation and Analysis Techniques

Using magnetic resonance imaging (MRI) in osteoarthritis pathogenesis research has proven extremely beneficial. However, it is always challenging for both clinicians and researchers to detect morphological changes in knee joints from magnetic resonance (MR) imaging since the surrounding tissues produce identical signals in MR studies, making it difficult to distinguish between them. Segmenting the knee bone, articular cartilage and menisci from the MR images allows one to examine the complete volume of the bone, articular cartilage, and menisci. It can also be used to assess certain characteristics quantitatively. However, segmentation is a laborious and time-consuming operation that requires sufficient training to complete correctly. With the advancement of MRI technology and computational methods, researchers have developed several algorithms to automate the task of individual knee bone, articular cartilage and meniscus segmentation during the last two decades. This systematic review aims to present available fully and semi-automatic segmentation methods for knee bone, cartilage, and meniscus published in different scientific articles. This review provides a vivid description of the scientific advancements to clinicians and researchers in this field of image analysis and segmentation, which helps the development of novel automated methods for clinical applications. The review also contains the recently developed fully automated deep learning-based methods for segmentation, which not only provides better results compared to the conventional techniques but also open a new field of research in Medical Imaging.

DADP: Dynamic abnormality detection and progression for longitudinal knee magnetic resonance images from the Osteoarthritis Initiative

Osteoarthritis (OA) is the most common disabling joint disease. Magnetic resonance (MR) imaging has been commonly used to assess knee joint degeneration due to its distinct advantage in detecting morphologic cartilage changes. Although several statistical methods over conventional radiography have been developed to perform quantitative cartilage analyses, little work has been done capturing the development and progression of cartilage lesions (or abnormal regions) and how they naturally progress. There are two major challenges, including (i) the lack of building spatial-temporal correspondences and correlations in cartilage thickness and (ii) the spatio-temporal heterogeneity in abnormal regions. The goal of this work is to propose a dynamic abnormality detection and progression (DADP) framework for quantitative cartilage analysis, while addressing the two challenges. First, spatial correspondences are established on flattened 2D cartilage thickness maps extracted from 3D knee MR images both across time within each subject and across all subjects. Second, a dynamic functional mixed effects model (DFMEM) is proposed to quantify abnormality progression across time points and subjects, while accounting for the spatio-temporal heterogeneity. We systematically evaluate our DADP using simulations and real data from the Osteoarthritis Initiative (OAI). Our results show that DADP not only effectively detects subject-specific dynamic abnormal regions, but also provides population-level statistical disease mapping and subgroup analysis.

Phenotypes of osteoarthritis: current state and future implications

In the most recent years, an extraordinary research effort has emerged to disentangle osteoarthritis heterogeneity, opening new avenues for progressing with therapeutic development and unravelling the pathogenesis of this complex condition. Several phenotypes and endotypes have been proposed albeit none has been sufficiently validated for clinical or research use as yet. This review discusses the latest advances in OA phenotyping including how new modern statistical strategies based on machine learning and big data can help advance this field of research.

Osteoarthritis year in review 2016: imaging

PURPOSE

The current narrative review covers original research related to imaging in osteoarthritis (OA) in humans published in English between April 1st 2015 and March 31st 2016, in peer reviewed journals available in Medline via PubMed (http://www.ncbi.nlm.nih.gov/pubmed/).

METHODS

Relevant studies in humans, subjectively decided by the authors, contributing significantly to the OA imaging field, were selected from an extensive Medline search using the terms "Osteoarthritis" in combination with "MRI", "Imaging", "Radiography", "X-rays", "Ultrasound", "Computed tomography", "Nuclear medicine", "PET-CT", "PET-MRI", "Scintigraphy", "SPECT". Publications were sorted according to relevance for the OA imaging research community with an emphasis on high impact special interest journals using the software for systematic reviews www.covidence.org.

RESULTS

An overview of newly published studies compared to studies reported previous years is presented, followed by a review of selected imaging studies of primarily knee, hip and hand OA focussing on (1) results for detection of OA and OA-related pathology (2) studies dealing with treatments and (3) studies focussing on prognosis of disease progression or joint replacement. A record high number of 1420 articles were published, among others, of new technologies and tools for improved morphological and pathophysiological understanding of OA-related changes in joints. Also, imaging data were presented of monitoring treatment effect and prognosis of OA progression, primarily using established radiographic, magnetic resonance imaging (MRI), and ultrasound (US) methods.

CONCLUSION

Imaging continues to play an important role in OA research, where several exciting new technologies and computer aided analysis methods are emerging to complement the conventional imaging approaches.