Computer-aided Diagnosis In Medical Imaging Research Articles

Modal adaptive super-resolution for medical images via continual learning

Details related to tiny abnormal pathologies and textures are crucial for clinical experts and computer-aided diagnosis in medical imaging. Super-resolving medical images provide significant support for disease diagnosis. However, due to differences and diversity in tissue appearance or spatial resolution of images caused by the acquisition principles or parameters in various imaging techniques, it limits their applications in multi-modal medical imaging. To this end, we propose a multi-modal adaptive super-resolution algorithm for reconstructing medical images, named MAda-SR, which improves the traditional Adam optimizer into an adaptive optimizer in terms of parameter updates and optimization strategies. Additionally, we enhance the MSE loss by adjusting its weight space, thereby increasing the transfer potential between multimodal tasks and enabling more extended continual learning. With extensive experimental validation, we demonstrate that a single super-resolution model can handle various modalities of medical datasets without compromising performance. The results indicate that our proposed MAda-SR outperforms comparative methods in terms of controlling forgetting and relearning metrics, achieving both stability and plasticity. The source code is available at https://github.com/wuzheng2022/MAda-SR.

Best Practices for Artificial Intelligence and Machine Learning for Computer-Aided Diagnosis in Medical Imaging

The American Association of Physicists in Medicine (AAPM) Task Group (TG) 273 has been charged with developing recommendations on best practices for the development and performance assessment of computer-aided decision support systems. The TG report [1] addresses broad issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal was to bring attention to issues such as proper data collection and training and validation methods for ML algorithms, aiming to improve generalizability and reliability and thus accelerate the adoption of CAD-AI systems for clinical decision support. The report focuses on several developmental stages of CAD-AI: data collection, reference standards, model development, performance assessment, and translation to the clinic.

Deep learning-based artificial intelligence imaging probes for Alzheimers disease

Brain medical imaging is a main diagnosis method for Alzheimers disease (AD). But the method relies on the physicians manual analysis which is subjective and time consuming. In recent years, artificial intelligence (AI) technology has been widely applied in clinical diagnosis. This thesis is about the deep learning model to be designed to realize the computer-aided diagnosis of medical images. A model of densely connected network (DenseNet) as an AI technology, automatically learns the semantic features related to AD diagnosis on the brain MRI images from ADNI data. At the same time, for solving the limited medical image samples problem, the effective transfer learning technology was applied in the experiment. The final model result achieves 90.8% accuracy, 82.2% sensitivity and 96.1% specificity on the diagnostic task of AD, and the diagnostic accuracy is better than prevailing methods. Besides 80.4% accuracy, 52.2% sensitivity, and 84.8% specificity are achieved in the task of distinguishing progressive from stable MCI patients. This method can provide more accurate diagnosis results of Alzheimers disease expected for the clinical early auxiliary diagnosis.

Reconstruction and completion of high-resolution 3D cardiac shapes using anisotropic CMRI segmentations and continuous implicit neural representations

Since the onset of computer-aided diagnosis in medical imaging, voxel-based segmentation has emerged as the primary methodology for automatic analysis of left ventricle (LV) function and morphology in cardiac magnetic resonance images (CMRI). In standard clinical practice, simultaneous multi-slice 2D cine short-axis MR imaging is performed under multiple breath-holds resulting in highly anisotropic 3D images. Furthermore, sparse-view CMRI often lacks whole heart coverage caused by large slice thickness and often suffers from inter-slice misalignment induced by respiratory motion. Therefore, these volumes only provide limited information about the true 3D cardiac anatomy which may hamper highly accurate assessment of functional and anatomical abnormalities. To address this, we propose a method that learns a continuous implicit function representing 3D LV shapes by training an auto-decoder. For training, high-resolution segmentations from cardiac CT angiography are used. The ability of our approach to reconstruct and complete high-resolution shapes from manually or automatically obtained sparse-view cardiac shape information is evaluated by using paired high- and low-resolution CMRI LV segmentations. The results show that the reconstructed LV shapes have an unconstrained subvoxel resolution and appear smooth and plausible in through-plane direction. Furthermore, Bland-Altman analysis reveals that reconstructed high-resolution ventricle volumes are closer to the corresponding reference volumes than reference low-resolution volumes with bias of [limits of agreement] −3.51 [−18.87, 11.85] mL, and 12.96 [−10.01, 35.92] mL respectively. Finally, the results demonstrate that the proposed approach allows recovering missing shape information and can indirectly correct for limited motion-induced artifacts.

Generation of Musculoskeletal Ultrasound Images with Diffusion Models

The recent advances in deep learning have revolutionised computer-aided diagnosis in medical imaging. However, deep learning approaches to unveil their full potential require significant amounts of data, which can be a challenging task in some scientific fields, such as musculoskeletal ultrasound imaging, in which data privacy and security reasons can lead to important limitations in the acquisition and the distribution process of patients’ data. For this reason, different generative methods have been introduced to significantly reduce the required amount of real data by generating synthetic images, almost indistinguishable from the real ones. In this study, the power of the diffusion models is incorporated for the generation of realistic data from a small set of musculoskeletal ultrasound images in four different muscles. Afterwards, the similarity of the generated and real images is assessed with different types of qualitative and quantitative metrics that correspond well with human judgement. In particular, the histograms of pixel intensities of the two sets of images have demonstrated that the two distributions are statistically similar. Additionally, the well-established LPIPS, SSIM, FID, and PSNR metrics have been used to quantify the similarity of these sets of images. The two sets of images have achieved extremely high similarity scores in all these metrics. Subsequently, high-level features are extracted from the two types of images and visualized in a two-dimensional space for inspection of their structure and to identify patterns. From this representation, the two sets of images are hard to distinguish. Finally, we perform a series of experiments to assess the impact of the generated data for training a highly efficient Attention-UNet for the important clinical application of muscle thickness measurement. Our results depict that the synthetic data play a significant role in the model’s final performance and can lead to the improvement of the deep learning systems in musculoskeletal ultrasound.

AAPM task group report 273: Recommendations on best practices for ai and machine learning for computer-aided diagnosis in medical imaging.

Rapid advances in artificial intelligence (AI) and machine learning, and specifically in deep learning (DL) techniques, have enabled broad application of these methods in health care. The promise of the DL approach has spurred further interest in computer-aided diagnosis (CAD) development and applications using both 'traditional' machine learning methods and newer DL-based methods. We use the term CAD-AI to refer to this expanded clinical decision support environment that uses traditional and DL-based AI methods. Numerous studies have been published to date on the development of machine learning tools for computer-aided, or AI-assisted, clinical tasks. However, most of these machine learning models are not ready for clinical deployment. It is of paramount importance to ensure that a clinical decision support tool undergoes proper training and rigorous validation of its generalizability and robustness before adoption for patient care in the clinic. To address these important issues, the American Association of Physicists in Medicine (AAPM) Computer-Aided Image Analysis Subcommittee (CADSC) is charged, in part, to develop recommendations on practices and standards for the development and performance assessment of computer-aided decision support systems. The committee has previously published two opinion papers on the evaluation of CAD systems and issues associated with user training and quality assurance of these systems in the clinic. With machine learning techniques continuing to evolve and CAD applications expanding to new stages of the patient care process, the current task group report considers the broader issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal is to bring attention to the proper training and validation of machine learning algorithms that may improve their generalizability and reliability and accelerate the adoption of CAD-AI systems for clinical decision support. This article is protected by copyright. All rights reserved.

Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors.

Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. The development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy. All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.

Computer-Aided Diagnosis of Medical Images in the Deep Learning Era

Computer-Aided Diagnosis of Medical Images in the Deep Learning Era

A Novel Radial Basis Neural Network-Leveraged Fast Training Method for Identifying Organs in MR Images.

We propose a new method for fast organ classification and segmentation of abdominal magnetic resonance (MR) images. Magnetic resonance imaging (MRI) is a new type of high-tech imaging examination fashion in recent years. Recognition of specific target areas (organs) based on MR images is one of the key issues in computer-aided diagnosis of medical images. Artificial neural network technology has made significant progress in image processing based on the multimodal MR attributes of each pixel in MR images. However, with the generation of large-scale data, there are few studies on the rapid processing of large-scale MRI data. To address this deficiency, we present a fast radial basis function artificial neural network (Fast-RBF) algorithm. The importance of our efforts is as follows: (1) The proposed algorithm achieves fast processing of large-scale image data by introducing the ε-insensitive loss function, the structural risk term, and the core-set principle. We apply this algorithm to the identification of specific target areas in MR images. (2) For each abdominal MRI case, we use four MR sequences (fat, water, in-phase (IP), and opposed-phase (OP)) and the position coordinates (x, y) of each pixel as the input of the algorithm. We use three classifiers to identify the liver and kidneys in the MR images. Experiments show that the proposed method achieves a higher precision in the recognition of specific regions of medical images and has better adaptability in the case of large-scale datasets than the traditional RBF algorithm.

Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging

Medical images (MI) are relevant sources of information for detecting and diagnosing a large number of illnesses and abnormalities. Due to their importance, this study is focused on breast ultrasound (BUS), which is the main adjunct for mammography to detect common breast lesions among women worldwide. On the other hand, aiming to enhance data security, image fidelity, authenticity, and content verification in e-health environments, MI watermarking has been widely used, whose main goal is to embed patient meta-data into MI so that the resulting image keeps its original quality. In this sense, this paper deals with the comparison of two watermarking approaches, namely spread spectrum based on the discrete cosine transform (SS-DCT) and the high-capacity data-hiding (HCDH) algorithm, so that the watermarked BUS images are guaranteed to be adequate for a computer-aided diagnosis (CADx) system, whose two principal outcomes are lesion segmentation and classification. Experimental results show that HCDH algorithm is highly recommended for watermarking medical images, maintaining the image quality and without introducing distortion into the output of CADx.

AMELIORATE FUZZY C-MEANS: AN AMELIORATE FUZZY C-MEANS CLUSTERING ALGORITHM FOR CT-LUNG IMAGE SEGMENTATION

Effective and efficient image segmentation acts as a preliminary stage for the computer-aided diagnosis of medical images. For image segmentation, many FCM-based clustering techniques have been proposed. Regrettably, the existing FCM technique does not generate accurate and standardized segmentation results. This is due to the noise present in the image as well as the random initialization of membership values for pixels. To address this issue, this study has enhanced the existing FCM technique and proposed a technique named Ameliorate FCM (AFCM). Initially, the given image is preprocessed to remove the noise using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. The preprocessed image is given as input to a Bayesian classifier to classify the images into two set namely normal and abnormal using a Hybrid feature selection method. The classified images are given as input to the proposed segmentation technique, which overcomes the drawbacks of existing FCM technique. Here, the membership value of the pixels of an image is standardized and clustered to segment the regions. Experiments are carried out using lung images to determine the efficiency of the proposed technique. Results of the experiment show that the proposed technique outperforms the existing FCM technique.

Extraction of media and plaque boundaries in intravascular ultrasound images by level sets and min/max flow

: Estimation of the plaque area in intravascular ultrasound images after extraction of the media and plaque–lumen interfaces is an important application of computer-aided diagnosis in medical imaging. This paper presents a novel system for fully automatic and fast calculation of plaque quantity by capturing the surrounding ring called media. The system utilizes an algorithm that consists of an enhanced technique for noise removal and a method of detecting different iso levels by sinking the image gradually under zero level. Moreover, an important novelty with this technique is the simultaneous extraction of media and lumen–plaque interfaces at satisfactory levels. There are no higher dimensional surfaces and evolution of contours, stopping at high image gradients. Thus, the system runs really fast with curvature velocity only and has no complexity. Experiments also show that this shape-recovering curvature term not only removes the noisy behaviour of ultrasound images but also strengthens very weak boundaries and even completes the missing walls of the media. In addition, the lumen–plaque interface can be detected simultaneously. For validation, a new and very useful algorithm is developed for labelling of intravascular ultrasound images, taken from video sequences of 15 patients, and a comparison-based verification is done between manual contours by experts and the contours extracted by our system.

A Novel Hybrid Linear/Nonlinear Classifier for Two-Class Classification: Theory, Algorithm, and Applications

Classifier design for a given classification task needs to take into consideration both the complexity of the classifier and the size of the dataset that is available for training the classifier. With limited training data, as often is the situation in computer-aided diagnosis of medical images, a classifier with simple structure (e.g., a linear classifier) is more robust and therefore preferred. We propose a novel two-class classifier, which we call a hybrid linear/nonlinear classifier (HLNLC), that involves two stages: the input features are linearly combined to form a scalar variable in the first stage and then the likelihood ratio of the scalar variable is used as the decision variable for classification. We first develop the theory of HLNLC by assuming that the feature data follow normal distributions. We show that the commonly used Fisher's linear discriminant function is generally not the optimal linear function in the first stage of the HLNLC. We formulate an optimization problem to solve for the optimal linear function in the first stage of the HLNLC, i.e., the linear function that maximizes the area under the receiver operating characteristic (ROC) curve of the HLNLC. For practical applications, we propose a robust implementation of the HLNLC by making a loose assumption that the two-class feature data arise from a pair of latent (rather than explicit) multivariate normal distributions. The novel hybrid classifier fills a gap between linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) in the sense that both its theoretical performance and its complexity lie between those of the LDA and those of the QDA. Simulation studies show that the hybrid linear/nonlinear classifier performs better than LDA without increasing the classifier complexity accordingly. With a finite number of training samples, the HLNLC can perform better than that of the ideal observer due to its simplicity. Finally, we demonstrate the application of the HLNLC in computer-aided diagnosis of breast lesions in ultrasound images.

Development of a Common Platform for Computer-Aided Diagnosis of Medical Images

Development of a Common Platform for Computer-Aided Diagnosis of Medical Images

Computer-aided diagnosis in medical imaging: Historical review, current status and future potential

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. In this article, the motivation and philosophy for early development of CAD schemes are presented together with the current status and future potential of CAD in a PACS environment. With CAD, radiologists use the computer output as a “second opinion” and make the final decisions. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral chest images has the potential to improve the overall performance in the detection of lung nodules when combined with another CAD scheme for PA chest images. Because vertebral fractures can be detected reliably by computer on lateral chest radiographs, radiologists’ accuracy in the detection of vertebral fractures would be improved by the use of CAD, and thus early diagnosis of osteoporosis would become possible. In MRA, a CAD system has been developed for assisting radiologists in the detection of intracranial aneurysms. On successive bone scan images, a CAD scheme for detection of interval changes has been developed by use of temporal subtraction images. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for chest CAD may include the computerized detection of lung nodules, interstitial opacities, cardiomegaly, vertebral fractures, and interval changes in chest radiographs as well as the computerized classification of benign and malignant nodules and the differential diagnosis of interstitial lung diseases. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with known pathology, which would be very similar to a new unknown case, from PACS when a reliable and useful method has been developed for quantifying the similarity of a pair of images for visual comparison by radiologists.

Current status and future potential of computer-aided diagnosis in medical imaging

Computer-aided diagnosis (CAD) has become one of the major research subjects in medical imaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinion to assist radiologists' image interpretation by improving the accuracy and consistency of radiological diagnosis and also by reducing the image reading time. In this article, a number of CAD schemes are presented, with emphasis on potential clinical applications. These schemes include: (1) detection and classification of lung nodules on digital chest radiographs; (2) detection of nodules in low dose CT; (3) distinction between benign and malignant nodules on high resolution CT; (4) usefulness of similar images for distinction between benign and malignant lesions; (5) quantitative analysis of diffuse lung diseases on high resolution CT; and (6) detection of intracranial aneurysms in magnetic resonance angiography. Because CAD can be applied to all imaging modalities, all body parts and all kinds of examinations, it is likely that CAD will have a major impact on medical imaging and diagnostic radiology in the 21st century.

Present and Future of Computer-Aided Diagnosis of Medical Images

Present and Future of Computer-Aided Diagnosis of Medical Images

Skeletal pattern extraction of bone trabeculae using mathematical morphology

Studies of morphology in extracting skeletal paterns of spiculae in mammograms served as a theoretical framework. Using studies of morphology in extraction of skeletal patterns of spiculae in mammograms as a theoretical framework, we applied a morphological filter (a combination of a skeleton operation and a single structuring element) to the extraction of skeletal patterns of bone trabeculae on computed radiographic (CR) images. Then, the comparison of the original images with the extracted skeletal patterns and the relationship between the sequence number(n) of the operation and the extracted skeletal patterns was reviewed. The comparison showed that skeletal patterns of the original image were extracted as binary images when using a morphological filter, and that, when n was small (n=0 or 1), skeletal patterns of both fine and wider bone trabeculae were extracted. Furthermore, with an increase in n, skeletal patterns of fine bone were eliminated, and only wider skeletal patterns were extracted selectively. These findings suggest that development of a suitable morphological filter to extract the skeletal patterns of bone trabeculae may be an effective supplement for computer aided diagnosis of medical images (CADM) in the support of adequate diagnosis of bone diseases.

Annual Joint Conference: 6th Conference of Japan Society of Computer Aided Diagnosis of Medical Images (CADM), Shinji Yamamoto, Professor, Toyohashi University of Technology, Conference Chair and 5th Conference of Japan Society of Computer Aided Surgery (CAS-Japan), Jun-ichiro Toriwaki, Professor, Nagoya University, Conference Chair

Annual Joint Conference: 6th Conference of Japan Society of Computer Aided Diagnosis of Medical Images (CADM), Shinji Yamamoto, Professor, Toyohashi University of Technology, Conference Chair and 5th Conference of Japan Society of Computer Aided Surgery (CAS-Japan), Jun-ichiro Toriwaki, Professor, Nagoya University, Conference Chair

Annual Joint Conference 6th Conference of Japan Society of Computer Aided Diagnosis of Medical Images (CADM), Shinji Yamamoto, Professor, Toyohashi University of Technology, Conference Chair 5 th Conference of Japan Society of Computer Aided Surgery (CAS-Japan), Jun-ichiro Toriwaki, Professor, Nagoya University, Conference Chair

Annual Joint Conference 6th Conference of Japan Society of Computer Aided Diagnosis of Medical Images (CADM), Shinji Yamamoto, Professor, Toyohashi University of Technology, Conference Chair 5 th Conference of Japan Society of Computer Aided Surgery (CAS-Japan), Jun-ichiro Toriwaki, Professor, Nagoya University, Conference Chair