Displaying publications 1 - 20 of 35 in total

  1. Rahimi A, Khalil A, Faisal A, Lai KW
    Curr Med Imaging, 2021;18(1):61-66.
    PMID: 34433403 DOI: 10.2174/1573405617666210825155659
    BACKGROUND: Early diagnosis of liver cancer may increase life expectancy. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play a vital role in diagnosing liver cancer. Together, both modalities offer significant individual and specific diagnosis data to physicians; however, they lack the integration of both types of information. To address this concern, a registration process has to be utilized for the purpose, as multimodal details are crucial in providing the physician with complete information.

    OBJECTIVE: The aim was to present a model of CT-MRI registration used to diagnose liver cancer, specifically for improving the quality of the liver images and provide all the required information for earlier detection of the tumors. This method should concurrently address the issues of imaging procedures for liver cancer to fasten the detection of the tumor from both modalities.

    METHODS: In this work, a registration scheme for fusing the CT and MRI liver images is studied. A feature point-based method with normalized cross-correlation has been utilized to aid in the diagnosis of liver cancer and provide multimodal information to physicians. Data on ten patients from an online database were obtained. For each dataset, three planar views from both modalities were interpolated and registered using feature point-based methods. The registration of algorithms was carried out by MATLAB (vR2019b, Mathworks, Natick, USA) on an Intel (R) Core (TM) i5-5200U CPU @ 2.20 GHz computer. The accuracy of the registered image is being validated qualitatively and quantitatively.

    RESULTS: The results show that an accurate registration is obtained with minimal distance errors by which CT and MRI were accurately registered based on the validation of the experts. The RMSE ranges from 0.02 to 1.01 for translation, which is equivalent in magnitude to approximately 0 to 5 pixels for CT and registered image resolution.

    CONCLUSION: The CT-MRI registration scheme can provide complementary information on liver cancer to physicians, thus improving the diagnosis and treatment planning process.

  2. Khalil A, Ng SC, Liew YM, Lai KW
    Cardiol Res Pract, 2018;2018:1437125.
    PMID: 30159169 DOI: 10.1155/2018/1437125
    Image registration has been used for a wide variety of tasks within cardiovascular imaging. This study aims to provide an overview of the existing image registration methods to assist researchers and impart valuable resource for studying the existing methods or developing new methods and evaluation strategies for cardiac image registration. For the cardiac diagnosis and treatment strategy, image registration and fusion can provide complementary information to the physician by using the integrated image from these two modalities. This review also contains a description of various imaging techniques to provide an appreciation of the problems associated with implementing image registration, particularly for cardiac pathology intervention and treatments.
  3. Yong CW, Lai KW, Murphy BP, Hum YC
    Curr Med Imaging, 2021;17(8):981-987.
    PMID: 33319690 DOI: 10.2174/1573405616666201214122409
    BACKGROUND: Osteoarthritis (OA) is a common degenerative joint inflammation that may lead to disability. Although OA is not lethal, this disease will remarkably affect patient's mobility and their daily lives. Detecting OA at an early stage allows for early intervention and may slow down disease progression.

    INTRODUCTION: Magnetic resonance imaging is a useful technique to visualize soft tissues within the knee joint. Cartilage delineation in magnetic resonance (MR) images helps in understanding the disease progressions. Convolutional neural networks (CNNs) have shown promising results in computer vision tasks, and various encoder-decoder-based segmentation neural networks are introduced in the last few years. However, the performances of such networks are unknown in the context of cartilage delineation.

    METHODS: This study trained and compared 10 encoder-decoder-based CNNs in performing cartilage delineation from knee MR images. The knee MR images are obtained from the Osteoarthritis Initiative (OAI). The benchmarking process is to compare various CNNs based on physical specifications and segmentation performances.

    RESULTS: LadderNet has the least trainable parameters with the model size of 5 MB. UNetVanilla crowned the best performances by having 0.8369, 0.9108, and 0.9097 on JSC, DSC, and MCC.

    CONCLUSION: UNetVanilla can be served as a benchmark for cartilage delineation in knee MR images, while LadderNet served as an alternative if there are hardware limitations during production.

  4. Latha S, Muthu P, Lai KW, Khalil A, Dhanalakshmi S
    Front Aging Neurosci, 2021;13:828214.
    PMID: 35153728 DOI: 10.3389/fnagi.2021.828214
    Atherosclerotic plaque deposit in the carotid artery is used as an early estimate to identify the presence of cardiovascular diseases. Ultrasound images of the carotid artery are used to provide the extent of stenosis by examining the intima-media thickness and plaque diameter. A total of 361 images were classified using machine learning and deep learning approaches to recognize whether the person is symptomatic or asymptomatic. CART decision tree, random forest, and logistic regression machine learning algorithms, convolutional neural network (CNN), Mobilenet, and Capsulenet deep learning algorithms were applied in 202 normal images and 159 images with carotid plaque. Random forest provided a competitive accuracy of 91.41% and Capsulenet transfer learning approach gave 96.7% accuracy in classifying the carotid artery ultrasound image database.
  5. Abu Bakar AR, Lai KW, Hamzaid NA
    Neurosci Lett, 2021 11 20;765:136250.
    PMID: 34536511 DOI: 10.1016/j.neulet.2021.136250
    Hearing loss is a common neurodegenerative disease that can start at any stage of life. Misalignment of the auditory neural impairment may impose challenges in processing incoming auditory stimulus that can be measured using electroencephalography (EEG). The electrophysiological behaviour response emanated from EEG auditory evoked potential (AEP) requires highly trained professionals for analysis and interpretation. Reliable automated methods using techniques of machine learning would assist the auditory assessment process for informed treatment and practice. It is thus highly required to develop models that are more efficient and precise by considering the characteristics of brain signals. This study aims to provide a comprehensive review of several state-of-the-art techniques of machine learning that adopt EEG evoked response for the auditory assessment within the last 13 years. Out of 161 initially screened articles, 11 were retained for synthesis. The outcome of the review presented that the Support Vector Machine (SVM) classifier outperformed with over 80% accuracy metric and was recognized as the best suited model within the field of auditory research. This paper discussed the comprehensive iterative properties of the proposed computed algorithms and the feasible future direction in hearing impaired rehabilitation.
  6. Khalil A, Faisal A, Ng SC, Liew YM, Lai KW
    J Med Imaging (Bellingham), 2017 Jul;4(3):037001.
    PMID: 28840172 DOI: 10.1117/1.JMI.4.3.037001
    A registration method to fuse two-dimensional (2-D) echocardiography images with cardiac computed tomography (CT) volume is presented. The method consists of two major procedures: temporal and spatial registrations. In temporal registration, the echocardiography frames at similar cardiac phases as the CT volume were interpolated based on electrocardiogram signal information, and the noise of the echocardiography image was reduced using the speckle reducing anisotropic diffusion technique. For spatial registration, an intensity-based normalized mutual information method was applied with a pattern search optimization algorithm to produce an interpolated cardiac CT image. The proposed registration framework does not require optical tracking information. Dice coefficient and Hausdorff distance for the left atrium assessments were [Formula: see text] and [Formula: see text], respectively; for left ventricle, they were [Formula: see text] and [Formula: see text], respectively. There was no significant difference in the mitral valve annulus diameter measurement between the manually and automatically registered CT images. The transformation parameters showed small deviations ([Formula: see text] deviation in translation and [Formula: see text] for rotation) between manual and automatic registrations. The proposed method aids the physician in diagnosing mitral valve disease as well as provides surgical guidance during the treatment procedure.
  7. Khalil A, Faisal A, Lai KW, Ng SC, Liew YM
    Med Biol Eng Comput, 2017 Aug;55(8):1317-1326.
    PMID: 27830464 DOI: 10.1007/s11517-016-1594-6
    This study proposed a registration framework to fuse 2D echocardiography images of the aortic valve with preoperative cardiac CT volume. The registration facilitates the fusion of CT and echocardiography to aid the diagnosis of aortic valve diseases and provide surgical guidance during transcatheter aortic valve replacement and implantation. The image registration framework consists of two major steps: temporal synchronization and spatial registration. Temporal synchronization allows time stamping of echocardiography time series data to identify frames that are at similar cardiac phase as the CT volume. Spatial registration is an intensity-based normalized mutual information method applied with pattern search optimization algorithm to produce an interpolated cardiac CT image that matches the echocardiography image. Our proposed registration method has been applied on the short-axis "Mercedes Benz" sign view of the aortic valve and long-axis parasternal view of echocardiography images from ten patients. The accuracy of our fully automated registration method was 0.81 ± 0.08 and 1.30 ± 0.13 mm in terms of Dice coefficient and Hausdorff distance for short-axis aortic valve view registration, whereas for long-axis parasternal view registration it was 0.79 ± 0.02 and 1.19 ± 0.11 mm, respectively. This accuracy is comparable to gold standard manual registration by expert. There was no significant difference in aortic annulus diameter measurement between the automatically and manually registered CT images. Without the use of optical tracking, we have shown the applicability of this technique for effective fusion of echocardiography with preoperative CT volume to potentially facilitate catheter-based surgery.
  8. Faisal A, Ng SC, Goh SL, Lai KW
    Med Biol Eng Comput, 2018 Apr;56(4):657-669.
    PMID: 28849317 DOI: 10.1007/s11517-017-1710-2
    Quantitative thickness computation of knee cartilage in ultrasound images requires segmentation of a monotonous hypoechoic band between the soft tissue-cartilage interface and the cartilage-bone interface. Speckle noise and intensity bias captured in the ultrasound images often complicates the segmentation task. This paper presents knee cartilage segmentation using locally statistical level set method (LSLSM) and thickness computation using normal distance. Comparison on several level set methods in the attempt of segmenting the knee cartilage shows that LSLSM yields a more satisfactory result. When LSLSM was applied to 80 datasets, the qualitative segmentation assessment indicates a substantial agreement with Cohen's κ coefficient of 0.73. The quantitative validation metrics of Dice similarity coefficient and Hausdorff distance have average values of 0.91 ± 0.01 and 6.21 ± 0.59 pixels, respectively. These satisfactory segmentation results are making the true thickness between two interfaces of the cartilage possible to be computed based on the segmented images. The measured cartilage thickness ranged from 1.35 to 2.42 mm with an average value of 1.97 ± 0.11 mm, reflecting the robustness of the segmentation algorithm to various cartilage thickness. These results indicate a potential application of the methods described for assessment of cartilage degeneration where changes in the cartilage thickness can be quantified over time by comparing the true thickness at a certain time interval.
  9. Muthu P, Tan Y, Latha S, Dhanalakshmi S, Lai KW, Wu X
    Front Public Health, 2022;10:1030656.
    PMID: 36699937 DOI: 10.3389/fpubh.2022.1030656
    Assistive technology for the differently abled and older adults has made remarkable achievements in providing rehabilitative, adaptive, and assistive devices. It provides huge assistance for people with physical impairments to lead a better self-reliant daily life, in terms of mobility, education, rehabilitation, etc. This technology ranges from simple hand-held devices to complex robotic accessories which promote the individual's independence. This study aimed at identifying the assistance required by differently-abled individuals, and the solutions proposed by different researchers, and reviewed their merits and demerits. It provides a detailed discussion on the state of art assistive technologies, their applications, challenges, types, and their usage for rehabilitation. The study also identifies different unexplored research areas related to assistive technology that can improve the daily life of individuals and advance the field. Despite their high usage, assistive technologies have some limitations which have been briefly described in the study. This review, therefore, can help understand the utilization, and pros and cons of assistive devices in rehabilitation engineering and assistive technologies.
  10. Li C, Yang M, Zhang Y, Lai KW
    Int J Environ Res Public Health, 2022 Nov 14;19(22).
    PMID: 36429697 DOI: 10.3390/ijerph192214976
    PURPOSE: Mental health assessments that combine patients' facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, which combines artificial intelligence technologies to assist in evaluating the mental health problems of college students.

    MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.

    RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.

    CONCLUSION: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.

  11. Wahab AA, Salim MI, Ahamat MA, Manaf NA, Yunus J, Lai KW
    Med Biol Eng Comput, 2016 Sep;54(9):1363-73.
    PMID: 26463520 DOI: 10.1007/s11517-015-1403-7
    Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.
  12. Shoaib MA, Hossain MB, Hum YC, Chuah JH, Mohd Salim MI, Lai KW
    Curr Med Imaging, 2020;16(6):739-751.
    PMID: 32723246 DOI: 10.2174/1573405615666190903143330
    BACKGROUND: Ultrasound (US) imaging can be a convenient and reliable substitute for magnetic resonance imaging in the investigation or screening of articular cartilage injury. However, US images suffer from two main impediments, i.e., low contrast ratio and presence of speckle noise.

    AIMS: A variation of anisotropic diffusion is proposed that can reduce speckle noise without compromising the image quality of the edges and other important details.

    METHODS: For this technique, four gradient thresholds were adopted instead of one. A new diffusivity function that preserves the edge of the resultant image is also proposed. To automatically terminate the iterative procedures, the Mean Absolute Error as its stopping criterion was implemented.

    RESULTS: Numerical results obtained by simulations unanimously indicate that the proposed method outperforms conventional speckle reduction techniques. Nevertheless, this preliminary study has been conducted based on a small number of asymptomatic subjects.

    CONCLUSION: Future work must investigate the feasibility of this method in a large cohort and its clinical validity through testing subjects with a symptomatic cartilage injury.

  13. Teo K, Yong CW, Chuah JH, Hum YC, Tee YK, Xia K, et al.
    Arab J Sci Eng, 2021 Aug 16.
    PMID: 34422543 DOI: 10.1007/s13369-021-06040-5
    Hospital readmission shortly after discharge threatens the quality of patient care and leads to increased medical care costs. In the United States, hospitals with high readmission rates are subject to federal financial penalties. This concern calls for incentives for healthcare facilities to reduce their readmission rates by predicting patients who are at high risk of readmission. Conventional practices involve the use of rule-based assessment scores and traditional statistical methods, such as logistic regression, in developing risk prediction models. The recent advancements in machine learning driven by improved computing power and sophisticated algorithms have the potential to produce highly accurate predictions. However, the value of such models could be overrated. Meanwhile, the use of other flexible models that leverage simple algorithms offer great transparency in terms of feature interpretation, which is beneficial in clinical settings. This work presents an overview of the current trends in risk prediction models developed in the field of readmission. The various techniques adopted by researchers in recent years are described, and the topic of whether complex models outperform simple ones in readmission risk stratification is investigated.
  14. Shazia A, Xuan TZ, Chuah JH, Usman J, Qian P, Lai KW
    PMID: 34335736 DOI: 10.1186/s13634-021-00755-1
    Coronavirus disease of 2019 or COVID-19 is a rapidly spreading viral infection that has affected millions all over the world. With its rapid spread and increasing numbers, it is becoming overwhelming for the healthcare workers to rapidly diagnose the condition and contain it from spreading. Hence it has become a necessity to automate the diagnostic procedure. This will improve the work efficiency as well as keep the healthcare workers safe from getting exposed to the virus. Medical image analysis is one of the rising research areas that can tackle this issue with higher accuracy. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet121, Inception-ResNet-V2, InceptionV3, Resnet50, and Xception) to deal with the detection and classification of coronavirus pneumonia from pneumonia cases. This study uses 7165 chest X-ray images of COVID-19 (1536) and pneumonia (5629) patients. Confusion metrics and performance metrics were used to analyze each model. Results show DenseNet121 (99.48% of accuracy) showed better performance when compared with the other models in this study.
  15. Nizar MHA, Chan CK, Khalil A, Yusof AKM, Lai KW
    Curr Med Imaging, 2020;16(5):584-591.
    PMID: 32484093 DOI: 10.2174/1573405615666190114151255
    BACKGROUND: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection.

    METHODS: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos.

    RESULTS: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models.

    CONCLUSION: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

  16. Zamzam AH, Abdul Wahab AK, Azizan MM, Satapathy SC, Lai KW, Hasikin K
    Front Public Health, 2021;9:753951.
    PMID: 34646808 DOI: 10.3389/fpubh.2021.753951
    Medical equipment highly contributes to the effectiveness of healthcare services quality. Generally, healthcare institutions experience malfunctioning and unavailability of medical equipment that affects the healthcare services delivery to the public. The problems are frequently due to a deficiency in managing and maintaining the medical equipment condition by the responsible party. The assessment of the medical equipment condition is an important activity during the maintenance and management of the equipment life cycle to increase availability, performance, and safety. The study aimed to perform a systematic review in extracting and categorising the input parameters applied in assessing the medical equipment condition. A systematic searching was undertaken in several databases, including Web of Science, Scopus, PubMed, Science Direct, IEEE Xplore, Emerald, Springer, Medline, and Dimensions, from 2000 to 2020. The searching processes were conducted in January 2020. A total of 16 articles were included in this study by adopting Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The review managed to classify eight categories of medical equipment reliability attributes, namely equipment features, function, maintenance requirement, performance, risk and safety, availability and readiness, utilisation, and cost. Applying the eight attributes extracted from computerised asset maintenance management system will assist the clinical engineers in assessing the reliability of medical equipment utilised in healthcare institution. The reliability assessment done in these eight attributes will aid clinical engineers in executing a strategic maintenance action, which can increase the equipment's availability, upkeep the performance, optimise the resources, and eventually contributes in providing effective healthcare service to the community. Finally, the recommendations for future works are presented at the end of this study.
  17. Khalil A, Rahimi A, Luthfi A, Azizan MM, Satapathy SC, Hasikin K, et al.
    Front Public Health, 2021;9:752509.
    PMID: 34621723 DOI: 10.3389/fpubh.2021.752509
    A process that involves the registration of two brain Magnetic Resonance Imaging (MRI) acquisitions is proposed for the subtraction between previous and current images at two different follow-up (FU) time points. Brain tumours can be non-cancerous (benign) or cancerous (malignant). Treatment choices for these conditions rely on the type of brain tumour as well as its size and location. Brain cancer is a fast-spreading tumour that must be treated in time. MRI is commonly used in the detection of early signs of abnormality in the brain area because it provides clear details. Abnormalities include the presence of cysts, haematomas or tumour cells. A sequence of images can be used to detect the progression of such abnormalities. A previous study on conventional (CONV) visual reading reported low accuracy and speed in the early detection of abnormalities, specifically in brain images. It can affect the proper diagnosis and treatment of the patient. A digital subtraction technique that involves two images acquired at two interval time points and their subtraction for the detection of the progression of abnormalities in the brain image was proposed in this study. MRI datasets of five patients, including a series of brain images, were retrieved retrospectively in this study. All methods were carried out using the MATLAB programming platform. ROI volume and diameter for both regions were recorded to analyse progression details, location, shape variations and size alteration of tumours. This study promotes the use of digital subtraction techniques on brain MRIs to track any abnormality and achieve early diagnosis and accuracy whilst reducing reading time. Thus, improving the diagnostic information for physicians can enhance the treatment plan for patients.
  18. Kulathilake KASH, Abdullah NA, Sabri AQM, Lai KW
    PMID: 34777967 DOI: 10.1007/s40747-021-00405-x
    Computed Tomography (CT) is a widely use medical image modality in clinical medicine, because it produces excellent visualizations of fine structural details of the human body. In clinical procedures, it is desirable to acquire CT scans by minimizing the X-ray flux to prevent patients from being exposed to high radiation. However, these Low-Dose CT (LDCT) scanning protocols compromise the signal-to-noise ratio of the CT images because of noise and artifacts over the image space. Thus, various restoration methods have been published over the past 3 decades to produce high-quality CT images from these LDCT images. More recently, as opposed to conventional LDCT restoration methods, Deep Learning (DL)-based LDCT restoration approaches have been rather common due to their characteristics of being data-driven, high-performance, and fast execution. Thus, this study aims to elaborate on the role of DL techniques in LDCT restoration and critically review the applications of DL-based approaches for LDCT restoration. To achieve this aim, different aspects of DL-based LDCT restoration applications were analyzed. These include DL architectures, performance gains, functional requirements, and the diversity of objective functions. The outcome of the study highlights the existing limitations and future directions for DL-based LDCT restoration. To the best of our knowledge, there have been no previous reviews, which specifically address this topic.
  19. Yeoh PSQ, Lai KW, Goh SL, Hasikin K, Hum YC, Tee YK, et al.
    Comput Intell Neurosci, 2021;2021:4931437.
    PMID: 34804143 DOI: 10.1155/2021/4931437
    Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.
  20. Kulathilake KASH, Abdullah NA, Bandara AMRR, Lai KW
    J Healthc Eng, 2021;2021:9975762.
    PMID: 34552709 DOI: 10.1155/2021/9975762
    Low-dose Computed Tomography (LDCT) has gained a great deal of attention in clinical procedures due to its ability to reduce the patient's risk of exposure to the X-ray radiation. However, reducing the X-ray dose increases the quantum noise and artifacts in the acquired LDCT images. As a result, it produces visually low-quality LDCT images that adversely affect the disease diagnosing and treatment planning in clinical procedures. Deep Learning (DL) has recently become the cutting-edge technology of LDCT denoising due to its high performance and data-driven execution compared to conventional denoising approaches. Although the DL-based models perform fairly well in LDCT noise reduction, some noise components are still retained in denoised LDCT images. One reason for this noise retention is the direct transmission of feature maps through the skip connections of contraction and extraction path-based DL modes. Therefore, in this study, we propose a Generative Adversarial Network with Inception network modules (InNetGAN) as a solution for filtering the noise transmission through skip connections and preserving the texture and fine structure of LDCT images. The proposed Generator is modeled based on the U-net architecture. The skip connections in the U-net architecture are modified with three different inception network modules to filter out the noise in the feature maps passing over them. The quantitative and qualitative experimental results have shown the performance of the InNetGAN model in reducing noise and preserving the subtle structures and texture details in LDCT images compared to the other state-of-the-art denoising algorithms.
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