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Blockchain can be thought of as a distributed database allowing tracing of the origin of data, and who has manipulated a given data set in the past. Medical applications of blockchain technology are emerging. Blockchain has many potential applications in medical imaging, typically making use of the tracking of radiological or clinical data. Clinical applications of blockchain technology include the documentation of the contribution of different "authors" including AI algorithms to multipart reports, the documentation of the use of AI algorithms towards the diagnosis, the possibility to enhance the accessibility of relevant information in electronic medical records, and a better control of users over their personal health records. Applications of blockchain in research include a better traceability of image data within clinical trials, a better traceability of the contributions of image and annotation data for the training of AI algorithms, thus enhancing privacy and fairness, and potentially make imaging data for AI available in larger quantities. Blockchain also allows for dynamic consenting and has the potential to empower patients and giving them a better control who has accessed their health data. There are also many potential applications of blockchain technology for administrative purposes, like keeping track of learning achievements or the surveillance of medical devices. This article gives a brief introduction in the basic technology and terminology of blockchain technology and concentrates on the potential applications of blockchain in medical imaging. [ABSTRACT FROM AUTHOR]

The fundamental professional roles of radiographers and radiologists are focused on providing benefit to patients with our skills, while maintaining their safety at all times. There are numerous patient safety issues in radiology which must be considered. These encompass: protection from direct harm arising from the techniques and technologies we use; ensuring physical and psychological well-being of patients while under our care; maintaining the highest possible quality of service provision; and protecting the staff to ensure they can deliver safe services. This paper summarises the key categories of safety issues in the provision of radiology services, from the joint perspectives of radiographers and radiologists, and provides references for further reading in all major relevant areas. This is a joint statement of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS), published simultaneously in Insights into Imaging [DOI:10.1186/s13244-019-0721-y] and Radiography (DOI: 10.1016/j.radi.2019.01.009). [ABSTRACT FROM AUTHOR]

The fundamental professional roles of radiographers and radiologists are focused on providing benefit to patients with our skills, while maintaining their safety at all times. There are numerous patient safety issues in radiology which must be considered. These encompass: protection from direct harm arising from the techniques and technologies we use; ensuring physical and psychological well-being of patients while under our care; maintaining the highest possible quality of service provision; and protecting the staff to ensure they can deliver safe services. This paper summarises the key categories of safety issues in the provision of radiology services, from the joint perspectives of radiographers and radiologists, and provides references for further reading in all major relevant areas. This is a joint statement of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS), published simultaneously in Insights into Imaging [DOI:10.1186/s13244-019-0721-y] and Radiography (DOI: 10.1016/j.radi.2019.01.009). [ABSTRACT FROM AUTHOR]

When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical imaging technologies cannot meet current requirements of medical service. On the one hand, the mainstream 3D volumetric imaging modalities (e.g. CT and MRI) require patients to lie down during the imaging process. On the other hand, the imaging modalities conducted in an upright posture (e.g. radiograph) can only realize 2D projections, which lose the valid information of spinal anatomy and curvature. Developments of deep learning-based 3D reconstruction methods bring potential to overcome the limitations of the existing medical imaging technologies. To deal with the limitations of current medical imaging technologies as is described above, in this paper, we propose a novel deep learning framework, ReVerteR, which can realize automatic 3D Reconstruction of Vertebrae from orthogonal bi-planar Radiographs. With the utilization of self-attention mechanism and specially designed loss function combining Dice, Hausdorff, Focal, and MSE, ReVerteR can alleviate the sample-imbalance problem during the reconstruction process and realize the fusion of the centroid annotation and the focused vertebra. Furthermore, aiming at automatic and customized 3D spinal reconstruction in real-world scenarios, we extend ReVerteR to a clinical deployment-oriented framework, and develop an interactive interface with all functions in the framework integrated so as to enhance human–computer interaction during clinical decision-making. Extensive experiments and visualization conducted on our constructed datasets based on two benchmark datasets of spinal CT, VerSe 2019 and VerSe 2020, demonstrate the effectiveness of our proposed ReVerteR. In this paper, we propose an automatic 3D reconstruction method of vertebrae based on orthogonal bi-planar radiographs. With the 3D upright posture of the spine under natural weight bearing effectively constructed, our proposed method is expected to better support doctors make clinical decision during spine-related diagnosis and surgery. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing the field of medical imaging, holding the potential to shift medicine from a reactive "sick-care" approach to a proactive focus on healthcare and prevention. The successful development of AI in this domain relies on access to large, comprehensive, and standardized real-world datasets that accurately represent diverse populations and diseases. However, images and data are sensitive, and as such, before using them in any way the data needs to be modified to protect the privacy of the patients. This paper explores the approaches in the domain of five EU projects working on the creation of ethically compliant and GDPR-regulated European medical imaging platforms, focused on cancer-related data. It presents the individual approaches to the de-identification of imaging data, and describes the problems and the solutions adopted in each case. Further, lessons learned are provided, enabling future projects to optimally handle the problem of data de-identification. Critical relevance statement: This paper presents key approaches from five flagship EU projects for the de-identification of imaging and clinical data offering valuable insights and guidelines in the domain. Key Points: ΑΙ models for health imaging require access to large amounts of data. Access to large imaging datasets requires an appropriate de-identification process. This paper provides de-identification guidelines from the AI for health imaging (AI4HI) projects. [ABSTRACT FROM AUTHOR]

Abstract: Mobile devices (smartphones, tablets, etc.) have become key methods of communication, data access and data sharing for the population in the past decade. The technological capabilities of these devices have expanded very rapidly; for example, their in-built cameras have largely replaced conventional cameras. Their processing power is often sufficient to handle the large data sets of radiology studies and to manipulate images and studies directly on hand-held devices. Thus, they can be used to transmit and view radiology studies, often in locations remote from the source of the imaging data. They are not recommended for primary interpretation of radiology studies, but they facilitate sharing of studies for second opinions, viewing of studies and reports by clinicians at the bedside, etc. Other potential applications include remote participation in educational activity (e.g. webinars) and consultation of online educational content, e-books, journals and reference sources. Social-networking applications can be used for exchanging professional information and teaching. Users of mobile device must be aware of the vulnerabilities and dangers of their use, in particular regarding the potential for inappropriate sharing of confidential patient information, and must take appropriate steps to protect confidential data.Key Points: • Mobile devices have revolutionized communication in the past decade, and are now ubiquitous.• Mobile devices have sufficient processing power to manipulate and display large data sets of radiological images.• Mobile devices allow transmission & sharing of radiologic studies for purposes of second opinions, bedside review of images, teaching, etc.• Mobile devices are currently not recommended as tools for primary interpretation of radiologic studies.• The use of mobile devices for image and data transmission carries risks, especially regarding confidentiality, which must be considered. [ABSTRACT FROM AUTHOR]

The European Society of Radiology (ESR) established a Working Group on Value-Based Imaging (VBI WG) in August 2016 in response to developments in European healthcare systems in general, and the trend within radiology to move from volume- to value-based practice in particular. The value-based healthcare (VBH) concept defines 'value' as health outcomes achieved for patients relative to the costs of achieving them. Within this framework, value measurements start at the beginning of therapy; the whole diagnostic process is disregarded, and is considered only if it is the cause of errors or complications. Making the case for a new, multidisciplinary organisation of healthcare delivery centred on the patient, this paper establishes the diagnosis of disease as a first outcome in the interrelated activities of the healthcare chain. Metrics are proposed for measuring the quality of radiologists' diagnoses and the various ways in which radiologists provide value to patients, other medical specialists and healthcare systems at large. The ESR strongly believes value-based radiology (VBR) is a necessary complement to existing VBH concepts. The Society is determined to establish a holistic VBR programme to help European radiologists deal with changes in the evolution from volume- to value-based evaluation of radiological activities. Main Messages • Value-based healthcare defines value as patient's outcome over costs. • The VBH framework disregards the diagnosis as an outcome. • VBH considers diagnosis only if wrong or a cause of complications. • A correct diagnosis is the first outcome that matters to patients. • Metrics to measure radiologists' impacts on patient outcomes are key. • The value provided by radiology is multifaceted, going beyond exam volumes. [ABSTRACT FROM AUTHOR]

A knowledge of the complex phenomena that regulate T1 signal on Magnetic Resonance Imaging is essential in clinical practice for a more effective characterization of pathological processes. The authors review the physical basis of T1 Relaxation Time and the fundamental aspects of physics and chemistry that can influence this parameter. The main substances (water, fat, macromolecules, methemoglobin, melanin, Gadolinium, calcium) that influence T1 and the different MRI acquisition techniques that can be applied to enhance their presence in diagnostic images are then evaluated. An extensive case illustration of the different phenomena and techniques in the areas of CNS, abdomino-pelvic, and osteoarticular pathology is also proposed. Critical relevance statement: T1 relaxation time is strongly influenced by numerous factors related to tissue characteristics and the presence in the context of the lesions of some specific substances. An examination of these phenomena with extensive MRI exemplification is reported. Key Points: The purpose of the paper is to illustrate the chemical-physical basis of T1 Relaxation Time. MRI methods in accordance with the various clinical indications are listed. Several examples of clinical application in abdominopelvic and CNS pathology are reported. [ABSTRACT FROM AUTHOR]

The paper addresses the issue of ensuring the authenticity and copyright of medical images in telemedicine applications, with a specific emphasis on watermarking methods. While several systems only concentrate on identifying tampering in medical images, others also provide the capacity to restore the tampered regions upon detection. While several authentication techniques in medical imaging have successfully achieved their goals, previous research underscores a notable deficiency: the resilience of these schemes against unintentional attacks has not been sufficiently examined or emphasized in previous research. This indicates the need for further development and investigation in improving the robustness of medical image authentication techniques against unintentional attacks. This research proposes a Reversible-Zero Watermarking approach as a solution to address these problems. The new approach merges the advantages of both the reversible and zero watermarking techniques. This system is comprised of two parts. The first part is a zero-watermarking technique that uses VGG19-based feature extraction and watermark information to establish an ownership share. The second part incorporates this ownership share into the image in a reversible manner using a combination of a discrete wavelet transform, an integer wavelet transform, and a difference expansion. Research findings confirm that the suggested watermarking approach for medical images demonstrates substantial enhancements compared to current methodologies. Research findings indicate that NC values are often around 0.9 for different attacks, whereas BER values are close to 0. It demonstrates exceptional qualities in being imperceptible, distinguishable, and robust. Additionally, the system provides a persistent verification feature that functions independently of disputes or third-party storage, making it the preferred choice in the domain of medical image watermarking. [ABSTRACT FROM AUTHOR]

The secure transmission of medical images and related digital patient records on untrusted channels has recently become a focus in healthcare industries. Data hiding and encryption are important tools for this goal. This paper proposes a data-hiding method for medical images in the context of invisibility, robustness, security and low time cost. A dual watermarking is introduced to accomplish chaos-based encryption to ensure medical images' copyright protection and content security. First, a local binary pattern based on neighbouring pixels is used to compute an optimal value, called an embedding factor, for embedding both marks. Second, the host medical image is marked using the lifting wavelet transform, the lower–upper (LU) decomposition and singular value decomposition with an embedding factor to protect ownership. Last, the marked image is encrypted by using a 3D-chaotic map. The method is tested on two standard datasets, which is convenient for medical applications. Our experimental results and performance analysis demonstrate that the proposed scheme produces a peak signal-to-noise ratio (PSNR) and NC wat 1 / NC wat 2 of 54.82 dB and 0.9916/0.9928, respectively. Furthermore, the key space analysis of our encryption technique is greater than 2 100 , which provides a high protection degree against brute-force attacks. The proposed scheme achieves better robustness, imperceptibility, and security performance than previous schemes. Our method can reduce time costs by 98% (91% on average), which results in lower time costs than other conventional encryption techniques. [ABSTRACT FROM AUTHOR]

Super-resolution is an ill-posed inverse problem. It needs a self-similarity regularization term in its formulation to solve the issue. Researchers have studied self-similarity in natural images thoroughly and extended the results to medical data. This paper investigates the self-similarity property using a general affine model and the SSIM, PSNR, matrix rank, and mutual information measures. We study variations of self-similarity with image modality, inter/intra-resolution, tissue types, and acquisition phase. Based on our experiments, super-resolution algorithms that use self-similarity property achieve better results in CT images than MR data. Some organs, including the liver, have a stronger self-similarity compared to the brain and lung. The extension of our study prepares a comprehensive guide map to employ self-similarity properties in medical image processing algorithms. [ABSTRACT FROM AUTHOR]

Single particle imaging at atomic resolution is perhaps one of the most desired goals for ultrafast X-ray science with X-ray free-electron lasers. Such a capability would create great opportunity within the biological sciences, as high-resolution structural information of biosamples that may not crystallize is essential for many research areas therein. In this paper, we report on a comprehensive computational study of diffraction image formation during single particle imaging of a macromolecule, containing over one hundred thousand non-hydrogen atoms. For this study, we use a dedicated simulation framework, SIMEX, available at the European XFEL facility. Our results demonstrate the full feasibility of computational single-particle imaging studies for biological samples of realistic size. This finding is important as it shows that the SIMEX platform can be used for simulations to inform relevant single-particle-imaging experiments and help to establish optimal parameters for these experiments. This will enable more focused and more efficient single-particle-imaging experiments at XFEL facilities, making the best use of the resource-intensive XFEL operation. [ABSTRACT FROM AUTHOR]

Medical image fusion aims to fuse multiple images from a single or multiple imaging modes to enhance their corresponding clinical applications in diagnosing and evaluating medical problems, a trend that has attracted increasing attention. However, most recent medical image fusion methods require prior knowledge, making it difficult to select image features. In this paper, we propose a novel deep medical image fusion method based on a deep convolutional neural network (DCNN) for directly learning image features from original images. Specifically, source images are first decomposed by low rank representation to obtain the principal and salient components, respectively. Following that, the deep features are extracted from the decomposed principal components via DCNN and fused by a weighted-average rule. Then, considering the complementary between the salient components obtained by the low rank representation, a simple yet effective sum rule is designed to fuse the salient components. Finally, the fused result is obtained by reconstructing the principal and salient components. The experimental results demonstrate that the proposed method outperforms several state-of-the-art medical image fusion approaches in terms of both objective indices and visual quality. [ABSTRACT FROM AUTHOR]

Medical images contain sensitive information that will be shared with healthcare workers, patients, and researchers for diagnostic and research purposes. However, the security of these images during transmission is extremely important, especially after the COVID-19 pandemic. In diagnosis, MRI and CT images are two common medical images, and the two medical images complement each other to a certain extent. The fusion of these two images can help doctors diagnose more intuitively. This paper proposes a zero-watermarking encryption scheme with an authentication function based on multimodal medical image fusion. In addition, an improved three-dimensional Chebyshev and ICMIC closed-loop coupling mapping (3D-CICCM) is proposed and applied to the design of medical image encryption. This scheme mainly includes: medical image fusion based on bilateral filtering and local gradient energy, authentication based on the Fresnel transform, double random phase encryption based on the Fresnel transform and shuffle algorithm, and zero-watermark generation algorithm based on stationary wavelet transform. The experimental results prove the effectiveness of this scheme. [ABSTRACT FROM AUTHOR]

Terahertz imaging is unlocking unique capabilities for the analysis of cultural heritage artifacts. This paper uses terahertz time-domain imaging for the study of a gilded wooden artifact, providing a means to perform stratigraphic analysis, yielding information about the composition of the artifact, presence of certain materials identifiable through their THz spectral fingerprint, as well as alterations that have been performed over time. Due to the limited information that is available for many historic artifacts, the data that can be obtained through the presented technique can guide proper stewardship of the artifact, informing its long-term preservation. [ABSTRACT FROM AUTHOR]

Improved software for processing medical images has inspired tremendous interest in modern medicine in recent years. Modern healthcare equipment generates huge amounts of data, such as scanned medical images and computerized patient information, which must be secured for future use. Diversity in the healthcare industry, namely in the form of medical data, is one of the largest challenges for researchers. Cloud environment and the Block chain technology have both demonstrated their own use. The purpose of this study is to combine both technologies for safe and secure transaction. Storing or sending medical data through public clouds exposes information into potential eavesdropping, data breaches and unauthorized access. Encrypting data before transmission is crucial to mitigate these security risks. As a result, a Blockchain based Chaotic Arnold's cat map Encryption Scheme (BCAES) is proposed in this paper. The BCAES first encrypts the image using Arnold's cat map encryption scheme and then sends the encrypted image into Cloud Server and stores the signed document of plain image into blockchain. As blockchain is often considered more secure due to its distributed nature and consensus mechanism, data receiver will ensure data integrity and authenticity of image after decryption using signed document stored into the blockchain. Various analysis techniques have been used to examine the proposed scheme. The results of analysis like key sensitivity analysis, key space analysis, Information Entropy, histogram correlation of adjacent pixels, Number of Pixel Change Rate, Peak Signal Noise Ratio, Unified Average Changing Intensity, and similarity analysis like Mean Square Error, and Structural Similarity Index Measure illustrated that our proposed scheme is an efficient encryption scheme as compared to some recent literature. Our current achievements surpass all previous endeavors, setting a new standard of excellence. [ABSTRACT FROM AUTHOR]

The challenge of making flexible, standard, and early medical diagnoses is significant. However, some limitations are not fully overcome. First, the diagnosis rules established by medical experts or learned from a trained dataset prove static and too general. It leads to decisions that lack adaptive flexibility when finding new circumstances. Secondly, medical terminological interoperability is highly critical. It increases realism and medical progress and avoids isolated systems and the difficulty of data exchange, analysis, and interpretation. Third, criteria for diagnosis are often heterogeneous and changeable. It includes symptoms, patient history, demographic, treatment, genetics, biochemistry, and imaging. Symptoms represent a high-impact indicator for early detection. It is important that we deal with these symptoms differently, which have a great relationship with semantics, vary widely, and have linguistic information. This negatively affects early diagnosis decision-making. Depending on the circumstances, the diagnosis is made solo on imaging and some medical tests. In this case, although the accuracy of the diagnosis is very high, can these decisions be considered an early diagnosis or prove the condition is deteriorating? Our contribution in this paper is to present a real medical diagnostic system based on semantics, fuzzy, and dynamic decision rules. We attempt to integrate ontology semantics reasoning and fuzzy inference. It promotes fuzzy reasoning and handles knowledge representation problems. In complications and symptoms, ontological semantic reasoning improves the process of evaluating rules in terms of interpretability, dynamism, and intelligence. A real-world case study, ADNI, is presented involving the field of Alzheimer's disease (AD). The proposed system has indicated the possibility of the system to diagnose AD with an accuracy of 97.2%, 95.4%, 94.8%, 93.1%, and 96.3% for AD, LMCI, EMCI, SMC, and CN respectively. [ABSTRACT FROM AUTHOR]

The incidence of thyroid nodules is increasing year by year. Accurate determination of benign and malignant nodules is an important basis for formulating treatment plans. Ultrasonography is the most widely used methodology in the diagnosis of benign and malignant nodules, but diagnosis by doctors is highly subjective, and the rates of missed diagnosis and misdiagnosis are high. To improve the accuracy of clinical diagnosis, this paper proposes a new diagnostic model based on deep learning. The diagnostic model adopts the diagnostic strategy of localization-classification. First, the distribution laws of the nodule size and nodule aspect ratio are obtained through data statistics, a multiscale localization network structure is a priori designed, and the nodule aspect ratio is obtained from the positioning results. Then, uncropped ultrasound images and nodule area image are correspondingly input into a two-way classification network, and an improved attention mechanism is used to enhance the feature extraction performance. Finally, the deep features, the shallow features, and the nodule aspect ratio are fused, and a fully connected layer is used to complete the classification of benign and malignant nodules. The experimental dataset consists of 4021 ultrasound images, where each image has been labeled under the guidance of doctors, and the ratio of the training set, validation set, and test set sizes is close to 3:1:1. The experimental results show that the accuracy of the multiscale localization network reaches 93.74%, and that the accuracy, specificity, and sensitivity of the classification network reach 86.34%, 81.29%, and 90.48%, respectively. Compared with the champion model of the TNSCUI 2020 classification competition, the accuracy rate is 1.52 points higher. Therefore, the network model proposed in this paper can effectively diagnose benign and malignant thyroid nodules. [ABSTRACT FROM AUTHOR]

Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities. [ABSTRACT FROM AUTHOR]

Current Medical Image Visual Question Answering (Med-VQA) models often tend to exploit language bias instead of learning the multimodal features from both vision and language, which often suffers from the sparse data and bad performance. In this paper, we propose a new pre-trained multilevel fusion network based on Vision-conditioned reasoning and Bilinear attentions for Med-VQA (VB-MVQA). To augment vision data, we firstly incorporate Contrastive Language-Image Pre-training (CLIP) and attention mechanisms for effectively extracting medical image features. And then, the proposed VB-MVQA model applies multiple stacked attention layers and Bilinear Attention Network (BAN) to fuse the extracted image features and the question features extracted by Bidirectional Long Short-Term Memory(Bi-LSTM). On this basis, the proposed VB-MVQA model introduces vision-conditioned reasoning to guide the importance selection over multimodal fused features and further enhance the image semantic information for eliminating the language bias. Extensive experiments on three public benchmark datasets (VQA-RAD, SLAKE, and VQA-Med-2019) show that the proposed model outperforms state-of-the-art models by an average improvement of 11.08%, 5.28%, and 8.30%, and our proposed method achieves more significant accuracy than the baseline models for open-ended questions and more powerful for language-bias Med-VQA datasets. [ABSTRACT FROM AUTHOR]

Glaucoma is an acquired optic neuropathy, which can lead to irreversible vision loss. Deep learning(DL), especially convolutional neural networks(CNN), has achieved considerable success in the field of medical image recognition due to the availability of large-scale annotated datasets and CNNs. However, obtaining fully annotated datasets like ImageNet in the medical field is still a challenge. Meanwhile, single-modal approaches remain both unreliable and inaccurate due to the diversity of glaucoma disease types and the complexity of symptoms. In this paper, a new multimodal dataset for glaucoma is constructed and a new multimodal neural network for glaucoma diagnosis and classification (GMNNnet) is proposed aiming to address both of these issues. Specifically, the dataset includes the five most important types of glaucoma labels, electronic medical records and four kinds of high-resolution medical images. The structure of GMNNnet consists of three branches. Branch 1 consisting of convolutional, cyclic and transposition layers processes patient metadata, branch 2 uses Unet to extract features from glaucoma segmentation based on domain knowledge, and branch 3 uses ResFormer to directly process glaucoma medical images.Branch one and branch two are mixed together and then processed by the Catboost classifier. We introduce a gradient-weighted class activation mapping (Grad-GAM) method to increase the interpretability of the model and a transfer learning method for the case of insufficient training data,i.e.,fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. The results show that GMNNnet can better present the high-dimensional information of glaucoma and achieves excellent performance under multimodal data. [ABSTRACT FROM AUTHOR]

In recent times, widely understood spine diseases have advanced to one of the most urgetn problems where quick diagnosis and treatment are needed. To diagnose its specifics (e.g. to decide whether this is a scoliosis or sagittal imbalance) and assess its extend, various kind of imaging diagnostic methods (such as X-Ray, CT, MRI scan or ST) are used. However, despite their common use, some may be regarded as (to a level) invasive methods and there are cases where there are contraindications to using them. Besides, which is even more of a problem, these are very expensive methods and whilst their use for pure diagnostic purposes is absolutely valid, then due to their cost, they cannot rather be considered as tools which would be equally valid for bad posture screening programs purposes. This paper provides an initial evaluation of the alternative approach to the spine diseases diagnostic/screening using inertial measurement unit and we propose policy-based computing as the core for the inference systems. Although the methodology presented herein is potentially applicable to a variety of spine diseases, in the nearest future we will focus specifically on sagittal imbalance detection. [ABSTRACT FROM AUTHOR]

Computed Tomography (CT) has become a useful screening procedure to identify disease or injury within various regions of the human body. The human beings' health issues caused by CT radiation have attracted the interest of the researchers and academic community. Reducing the radiation dose is the solution, but the CT image generated with low-dose radiation results in excessive noise due to lower intensity and fewer angle measurements. Low-dose CT scan images reduce image quality and thus affect a doctor's diagnosis. Deep learning methods have become increasingly popular in recent years, many models have been proposed for Low-Dose CT image reconstruction. Low-Dose CT Image Reconstruction is an active area of modern medical imaging research. Deep learning-based medical image reconstruction methods will be helpful to reduce noise without compromising image quality. Therefore, this paper introduces a novel CT image reconstruction method based on the vector quantization technique utilized in the convolutional autoencoder network. The quality of the results is evaluated based on the perceptual loss function. Experimental evaluations are conducted on the LoDoPaB-CT benchmark dataset. Its result showed that the proposed network obtained better performance metric values and better noise elimination results, in terms of quantitative and visual evaluation, respectively. [ABSTRACT FROM AUTHOR]

This paper presents methods of decomposition of musculoskeletal structures from radiographs into multiple individual muscle and bone structures. While existing solutions require dual-energy scan for the training dataset and are mainly applied to structures with high-intensity contrast, such as bones, we focused on multiple superimposed muscles with subtle contrast in addition to bones. The decomposition problem is formulated as an image translation problem between (1) a real X-ray image and (2) multiple digitally reconstructed radiographs, each of which contains a single muscle or bone structure, and solved using unpaired training based on the CycleGAN framework. The training dataset was created via automatic computed tomography (CT) segmentation of muscle/bone regions and virtually projecting them with geometric parameters similar to the real X-ray images. Two additional features were incorporated into the CycleGAN framework to achieve a high-resolution and accurate decomposition: hierarchical learning and reconstruction loss with the gradient correlation similarity metric. Furthermore, we introduced a new diagnostic metric for muscle asymmetry directly measured from a plain X-ray image to validate the proposed method. Our simulation and real-image experiments using real X-ray and CT images of 475 patients with hip diseases suggested that each additional feature significantly enhanced the decomposition accuracy. The experiments also evaluated the accuracy of muscle volume ratio measurement, which suggested a potential application to muscle asymmetry assessment from an X-ray image for diagnostic and therapeutic assistance. The improved CycleGAN framework can be applied for investigating the decomposition of musculoskeletal structures from single radiographs. [ABSTRACT FROM AUTHOR]

The effective segmentation of the lesion region in gastric cancer images can assist physicians in diagnosing and reducing the probability of misdiagnosis. The U-Net has been proven to provide segmentation results comparable to specialists in medical image segmentation because of its ability to extract high-level semantic information. However, it has limitations in obtaining global contextual information. On the other hand, the Transformer excels at modeling explicit long-range relations but cannot capture low-level detail information. Hence, this paper proposes a Dual-Branch Hybrid Network based on the fusion Transformer and U-Net to overcome both limitations. We propose the Deep Feature Aggregation Decoder (DFA) by aggregating only the in-depth features to obtain salient lesion features for both branches and reduce the complexity of the model. Besides, we design a Feature Fusion (FF) module utilizing the multi-modal fusion mechanisms to interact with independent features of various modalities and the linear Hadamard product to fuse the feature information extracted from both branches. Finally, the Transformer loss, the U-Net loss, and the fused loss are compared to the ground truth label for joint training. Experimental results show that our proposed method has an IOU of 81.3%, a Dice coefficient of 89.5%, and an Accuracy of 94.0%. These metrics demonstrate that our model outperforms the existing models in obtaining high-quality segmentation results, which has excellent potential for clinical analysis and diagnosis. The code and implementation details are available at Github, https://github.com/ZYY01/DBH-Net/. [ABSTRACT FROM AUTHOR]

Medical image segmentation provides various effective methods for accuracy and robustness of organ segmentation, lesion detection, and classification. Medical images have fixed structures, simple semantics, and diverse details, and thus fusing rich multi-scale features can augment segmentation accuracy. Given that the density of diseased tissue may be comparable to that of surrounding normal tissue, both global and local information are critical for segmentation results. Therefore, considering the importance of multi-scale, global, and local information, in this paper, we propose the dynamic hierarchical multi-scale fusion network with axial mlp (multilayer perceptron) (DHMF-MLP), which integrates the proposed hierarchical multi-scale fusion (HMSF) module. Specifically, HMSF not only reduces the loss of detail information by integrating the features of each stage of the encoder, but also has different receptive fields, thereby improving the segmentation results for small lesions and multi-lesion regions. In HMSF, we not only propose the adaptive attention mechanism (ASAM) to adaptively adjust the semantic conflicts arising during the fusion process but also introduce Axial-mlp to improve the global modeling capability of the network. Extensive experiments on public datasets confirm the excellent performance of our proposed DHMF-MLP. In particular, on the BUSI, ISIC 2018, and GlaS datasets, IoU reaches 70.65%, 83.46%, and 87.04%, respectively. [ABSTRACT FROM AUTHOR]

Coronavirus 2019 (COVID-19) is a new acute respiratory disease that has spread rapidly throughout the world. This paper proposes a novel deep learning network based on ResNet-50 merged transformer named RMT-Net. On the backbone of ResNet-50, it uses Transformer to capture long-distance feature information, adopts convolutional neural networks and depth-wise convolution to obtain local features, reduce the computational cost and acceleration the detection process. The RMT-Net includes four stage blocks to realize the feature extraction of different receptive fields. In the first three stages, the global self-attention method is adopted to capture the important feature information and construct the relationship between tokens. In the fourth stage, the residual blocks are used to extract the details of feature. Finally, a global average pooling layer and a fully connected layer perform classification tasks. Training, verification and testing are carried out on self-built datasets. The RMT-Net model is compared with ResNet-50, VGGNet-16, i-CapsNet and MGMADS-3. The experimental results show that the RMT-Net model has a Test_ acc of 97.65% on the X-ray image dataset, 99.12% on the CT image dataset, which both higher than the other four models. The size of RMT-Net model is only 38.5 M, and the detection speed of X-ray image and CT image is 5.46 ms and 4.12 ms per image, respectively. It is proved that the model can detect and classify COVID-19 with higher accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Different deep learning-based architectures have been developed for medical image registration in the last few years. The architectures of these methods are complex and require a considerable amount of memory. The scalability of the architectures is limited due to their compatibility with low or moderate memory devices. The deformable image registration attained attention in the field of medical image registration. The uprising in deep learning has paved the path for more sophisticated solutions to the many medical imaging problems. We have proposed the resource-efficient and structure-preserving network (RESPNet) to solve medical image registration issues. RESPNet is convolutional neural networks (CNNs)-based architecture to predict the deformation vector field (DVF), which signifies the displacement of each pixel in all directions. We have developed the three CNNs modules (i.e., StraightCNN, UpCNN, and DownCNN) to reduce parameter size and preserve the structure of the images. In this paper, the architecture of RESPNet utilizes the structural properties of images for 2D registration of the retina images and 3D registration of brain magnetic resonance (MR) images. The proposed architecture requires less than 25% memory compared to the current state-of-the-art methods and can be trained in 6-8 hours on a 13 GB GPU to produce the results. The dice score of 2D and 3D images registration is 0.8784 and 0.7515, respectively. The suggested architecture reduces the memory requirements by more than 75% and achieves better performance in dice score, mutual information, and processing time. We have developed a memory-efficient deep learning architecture for medical image registration. This architecture can be employed to register 2D and 3D images efficiently. [ABSTRACT FROM AUTHOR]

The irrational coefficients of orthogonal wavelet filters require a large amount of hardware for their implementation. This results in huge memory requirement, increased power dissipation and reduction in speed of operation. This paper presents a new class of multiplier free db3 orthogonal wavelet filter bank with significantly reduced power dissipation, adders and shifters. In this paper, dyadic filter coefficients of db3 have been obtained by slightly altering double-shifting orthogonality property for perfect reconstruction. The proposed db3 wavelet filter improved time–frequency product, Sobolev regularity and frequency selectivity. Also, the VLSI architecture is suggested for the proposed db3 wavelet filter banks to analyse the hardware computational complexity. The performance of the proposed db3 wavelets achieved satisfactory performance for feature extraction in medical image retrieval and fingerprint recognition of infants and toddlers. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) with medical images to solve clinical problems is becoming increasingly common, and the development of new AI solutions is leading to more studies and publications using this computational technology. As a novel research area, the use of common standards to aid AI developers and reviewers as quality control criteria will improve the peer review process. Although some guidelines do exist, their heterogeneity and extension advocate that more explicit and simple schemes should be applied on the publication practice. Based on a review of existing AI guidelines, a proposal which collects, unifies, and simplifies the most relevant criteria was developed. The MAIC-10 (Must AI Criteria-10) checklist with 10 items was implemented as a guide to design studies and evaluate publications related to AI in the field of medical imaging. Articles published in Insights into Imaging in 2021 were selected to calculate their corresponding MAIC-10 quality score. The mean score was found to be 5.6 ± 1.6, with critical items present in most articles, such as "Clinical need", "Data annotation", "Robustness", and "Transparency" present in more than 80% of papers, while improvements in other areas were identified. MAIC-10 was also observed to achieve the highest intra-observer reproducibility when compared to other existing checklists, with an overall reduction in terms of checklist length and complexity. In summary, MAIC-10 represents a short and simple quality assessment tool which is objective, robust and widely applicable to AI studies in medical imaging. Key points: AI solutions have become an essential clinical tool in medical imaging. Standardised criteria are necessary to ensure quality of AI studies. Established criteria are often incomplete, too exhaustive, or not broadly applicable. A concise and reproducible quantitative checklist will help to ensure a minimum of acceptance. [ABSTRACT FROM AUTHOR]

Recent progress in encoder–decoder neural network architecture design has led to significant performance improvements in a wide range of medical image segmentation tasks. However, state-of-the-art networks for a given task may be too computationally demanding to run on affordable hardware, and thus users often resort to practical workarounds by modifying various macro-level design aspects. Two common examples are downsampling of the input images and reducing the network depth or size to meet computer memory constraints. In this paper, we investigate the effects of these changes on segmentation performance and show that image complexity can be used as a guideline in choosing what is best for a given dataset. We consider four statistical measures to quantify image complexity and evaluate their suitability on ten different public datasets. For the purpose of our illustrative experiments, we use DeepLabV3+ (deep large-size), M2U-Net (deep lightweight), U-Net (shallow large-size), and U-Net Lite (shallow lightweight). Our results suggest that median frequency is the best complexity measure when deciding on an acceptable input downsampling factor and using a deep versus shallow, large-size versus lightweight network. For high-complexity datasets, a lightweight network running on the original images may yield better segmentation results than a large-size network running on downsampled images, whereas the opposite may be the case for low-complexity images. [ABSTRACT FROM AUTHOR]

Megastability and coexisting attractors between periodic and self-excited chaotic attractors in the Josephson junction snap oscillator (JJSO) are reported in this paper. Two different cases are considered for this study. In one case, the JJSO is driven by a direct current (DC) whereas in the other case, it is driven by an alternative current (AC). The JJSO is implemented using a microcontroller to verify the numerical simulation results. The numerical simulations and microcontroller results have a qualitatively good agreement. Finally, an encryption scheme based on the modified Julia set, deoxyribonucleic acid (DNA) principle, and the sequences of the chaotic JJSO in this work are used to build a secure key to secure medical images. The plain colour image is decomposed into a blue component. The generated secure key is applied to one component to obtain the corresponding sparse components. Confusion keys are obtained from the chaotic JJSO to scramble each sparse component. Besides, the chaotic sequence is used to compress the confused sparse matrices corresponding to the blue components. Lastly, the component is quantified and a diffusion step is then applied to improve the randomness and consequently the information entropy. [ABSTRACT FROM AUTHOR]

In the past decades, CT images have been widely used and played a critical role in medical diagnosis. However, low-dose CT images are often contaminated by noise, this being the most important factor affecting the quality of a CT image. This paper proposes a novel integrated framework and a denoising method for low-dose medical CT images to obtain a better denoising effect whilst at the same time preserving an image's local structure information. First, an image moving decomposition is employed to decompose the CT image. The original CT noisy image is decomposed, and the components will be processed separately, so that the details and edges of the CT image can be better preserved. Next, the Shearlet Transformation-based denoising method is applied to the component which contains edges and detailed information of the CT image. The multi-directionality and multi-scale property of the Shearlet make it possible to obtain better effect in denoising the detail parts. BM3D filtering is used to remove noise in the component similar to the origin image, and obtain ideal denoising results in denoising the approximate components (mainly low frequency part) of the CT image. With the two processed components and the inverse decomposition, the denoised image is obtained. Finally, simulations and clinical experiments are conducted and comparisons made. The experimental results show the proposed denoising method can obtain better performances in terms of PSNR value, SSIM and FoM and thus have very competitive results compared with other existing CT denoising methods. [ABSTRACT FROM AUTHOR]

Large epidemiological studies such as the UK Biobank (UKBB) or German National Cohort (NAKO) provide unprecedented health-related data of the general population aiming to better understand determinants of health and disease. As part of these studies, Magnetic Resonance Imaging (MRI) is performed in a subset of participants allowing for phenotypical and functional characterization of different organ systems. Due to the large amount of imaging data, automated image analysis is required, which can be performed using deep learning methods, e. g. for automated organ segmentation. In this paper we describe a computational pipeline for automated segmentation of abdominal organs on MRI data from 20,000 participants of UKBB and NAKO and provide results of the quality control process. We found that approx. 90% of data sets showed no relevant segmentation errors while relevant errors occurred in a varying proportion of data sets depending on the organ of interest. Image-derived features based on automated organ segmentations showed relevant deviations of varying degree in the presence of segmentation errors. These results show that large-scale, deep learning-based abdominal organ segmentation on MRI data is feasible with overall high accuracy, but visual quality control remains an important step ensuring the validity of down-stream analyses in large epidemiological imaging studies. [ABSTRACT FROM AUTHOR]

Medical image security is a critical concern in the healthcare domain, and various watermarking techniques have been explored to embed imperceptible and secure data within medical images. This paper introduces an innovative frequency-based watermarking technique for medical images, utilizing the Fractional Discrete Cosine Transform (FDCT) and Schur decomposition to ensure robust and secure watermark embedding. The watermark bits are integrated by modulating the obtained Schur coefficients, thereby ensuring robust and secure watermarking without significantly altering the visual quality of the medical images. The experiments conducted on the ocular database demonstrate the capacity, imperceptibility, and robustness of the proposed method. This approach achieved a favorable trade-off between imperceptibility and information embedding capacity for ensuring the authenticity and integrity of medical images during transmission. [ABSTRACT FROM AUTHOR]

Background: Chronic nonbacterial osteomyelitis (CNO), also known as chronic recurrent multifocal osteomyelitis, is a noninfectious autoinflammatory disorder that occurs primarily in children and adolescents and is characterized by episodic musculoskeletal pain with a protracted course. Main body: Traditionally, the diagnosis of CNO is made by exclusion and commonly requires bone biopsy to rule out infection and malignancy. However, bone biopsy may be avoided when imaging and clinical characteristic features are present, such as multifocal bone lesions at typical sites, no constitutional symptoms and no signs of infection in laboratory test results. Whole-body magnetic resonance imaging (WB-MRI) can assess signs of acute and chronic inflammation and enables the detection of CNO typical patterns of lesion location and distribution, thereby helping to exclude differential diagnosis. The goal of the present study paper is to review the main clinical and imaging aspects of the disease with emphasis on the role of WB-MRI in the diagnosis, assessment of disease burden and follow-up monitoring. Conclusion: Radiologists need to be familiar with the imaging features to suggest the diagnosis as the early therapy may help to avoid irreversible secondary damage of skeletal system. [ABSTRACT FROM AUTHOR]

Grand challenges have become the de facto standard for benchmarking image analysis algorithms. While the number of these international competitions is steadily increasing, surprisingly little effort has been invested in ensuring high quality design, execution and reporting for these international competitions. Specifically, results analysis and visualization in the event of uncertainties have been given almost no attention in the literature. Given these shortcomings, the contribution of this paper is two-fold: (1) we present a set of methods to comprehensively analyze and visualize the results of single-task and multi-task challenges and apply them to a number of simulated and real-life challenges to demonstrate their specific strengths and weaknesses; (2) we release the open-source framework challengeR as part of this work to enable fast and wide adoption of the methodology proposed in this paper. Our approach offers an intuitive way to gain important insights into the relative and absolute performance of algorithms, which cannot be revealed by commonly applied visualization techniques. This is demonstrated by the experiments performed in the specific context of biomedical image analysis challenges. Our framework could thus become an important tool for analyzing and visualizing challenge results in the field of biomedical image analysis and beyond. [ABSTRACT FROM AUTHOR]

For medical image analysis, there is always an immense need for rich details in an image. Typically, the diagnosis will be served best if the fine details in the image are retained and the image is available in high resolution. In medical imaging, acquiring high-resolution images is challenging and costly as it requires sophisticated and expensive instruments, trained human resources, and often causes operation delays. Deep learning based super resolution techniques can help us to extract rich details from a low-resolution image acquired using the existing devices. In this paper, we propose a new Generative Adversarial Network (GAN) based architecture for medical images, which maps low-resolution medical images to high-resolution images. The proposed architecture is divided into three steps. In the first step, we use a multi-path architecture to extract shallow features on multiple scales instead of single scale. In the second step, we use a ResNet34 architecture to extract deep features and upscale the features map by a factor of two. In the third step, we extract features of the upscaled version of the image using a residual connection-based mini-CNN and again upscale the feature map by a factor of two. The progressive upscaling overcomes the limitation for previous methods in generating true colors. Finally, we use a reconstruction convolutional layer to map back the upscaled features to a high-resolution image. Our addition of an extra loss term helps in overcoming large errors, thus, generating more realistic and smooth images. We evaluate the proposed architecture on four different medical image modalities: (1) the DRIVE and STARE datasets of retinal fundoscopy images, (2) the BraTS dataset of brain MRI, (3) the ISIC skin cancer dataset of dermoscopy images, and (4) the CAMUS dataset of cardiac ultrasound images. The proposed architecture achieves superior accuracy compared to other state-of-the-art super-resolution architectures. [ABSTRACT FROM AUTHOR]

A multimedia-based medical decision-making system is an ultimate requirement in the medical imaging domain. In the healthcare sector, achieving quick and efficient results is one of the primary needs as well as a necessity for both doctors and patients. Liver cancer is increasingly spreading disease across the globe, and its timely diagnosis requires an accurate and precise tumor segmentation. However, it is very challenging to segment the tumors because of the variability in appearance, hazy borders, diverse densities, and sizes of tumors. Currently, deep learning-based approaches are being applied in a variety of domains which results in better performances. In this paper, a deep learning method based on modified U-Net referred to as Residual-Atrous U-Net (RA-Net) is proposed to segment the liver tumors. The suggested model is implemented by employing U-Net as a base model and extracts the tumor's features utilizing a parallel structure-based Atrous convolution block embedded in the original U-Net. This addresses the heterogeneity of tumors in terms of sizes as well as retaining the wider context without introducing any parameters to the model due to the various scales dilated kernels. Moreover, a strong, smooth, and non-monoatomic Mish activation function is deployed in this parallel Atrous block to bring nonlinearity. Besides this, the features of tumors are also extracted from Res Block simultaneously which learns the residual between the input and output feature maps via a skip link that jumps directly from input to output. This identity mapping in this block overcomes network degradation and increases performance. Later on, all these extracted features are fused to be sent to the network and hence improve the feature learning of the original U-Net. Furthermore, we have used the strategy of segmenting the tumors directly from the CT-scan instead of the two-stage process used by the majority of existing methods. We have performed experimental analysis over the 3DIRCADb dataset and according to the results shown by the proposed RA-Net, the Jaccard score which is a performance indicator stands out at 72%. [ABSTRACT FROM AUTHOR]

The rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model's predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity. [ABSTRACT FROM AUTHOR]

The use of deep learning in medical imaging has increased rapidly over the past few years, finding applications throughout the entire radiology pipeline, from improved scanner performance to automatic disease detection and diagnosis. These advancements have resulted in a wide variety of deep learning approaches being developed, solving unique challenges for various imaging modalities. This paper provides a review on these developments from a technical point of view, categorizing the different methodologies and summarizing their implementation. We provide an introduction to the design of neural networks and their training procedure, after which we take an extended look at their uses in medical imaging. We cover the different sections of the radiology pipeline, highlighting some influential works and discussing the merits and limitations of deep learning approaches compared to other traditional methods. As such, this review is intended to provide a broad yet concise overview for the interested reader, facilitating adoption and interdisciplinary research of deep learning in the field of medical imaging. [ABSTRACT FROM AUTHOR]

Access to imaging diagnostics has been shown to result in accurate treatment, management, and optimal outcomes. Particularly in low-income and low-middle-income countries (LICs, LMICs), access is limited due to a lack of adequate resources. To achieve Sustainable Development Goal (SDG) 3, access to imaging services is critical at every tier of the health system. Optimizing imaging services in low-resource settings is best accomplished by prescriptive, integrated, and coordinated tiered service delivery that takes contextual factors into consideration. To our knowledge, this is the first recommendation for optimized, specific imaging care delivery by tier. A model for tier-based essential imaging services informs and guides policymakers as they set priorities and make budgetary decisions. In this paper, we recommend a framework for tiered imaging services essential to reduce the global burden of disease and attain universal health coverage (UHC). A lack of access to basic imaging services, even at the lowest tier of the health system, can no longer be justified by cost. Worldwide, affordable modalities of modern ultrasound and X-ray are becoming an accessible mainstay for the investigation of common conditions such as pregnancy, pneumonia, and fractures, and are safely performed and interpreted by qualified professionals. Finally, given the vast gap in access to imaging resources between LMICs and high-income countries (HICs), a scale-up of tiered imaging services in low-resource settings has the potential to reduce health disparities between, and within countries. As the access to appropriately integrated imaging services improves, UHC may be achieved. [ABSTRACT FROM AUTHOR]

Objectives: To analyse the coverage and main bibliometric indicators of Insights into Imaging in Scopus and the Emerging Sources Citation Index (ESCI) from the Web of Science Core Collection database. Methods: The evolution of journal production in the Scopus database was analysed according to document types, collaboration indexes between authors and institutions, and citation indicators (number of citations, SCImago Journal Rank, quartile, h-index, and most cited works). Networks of collaboration among authors, institutions, and countries were also analysed, as well as those of co-word networks. As a complementary source of information, the Emerging Source Citation Index from the Web of Science database was also considered. Results: Four hundred seventy-four papers were included in Scopus and 292 in ESCI. The index of collaboration was 5.18 for authors and 2.74 for institutions. International collaboration occurred in 24.7% of the papers. The number of citations received in Scopus (4295) exceeds the number of citations received in ESCI (1177). The average number of citations per paper was 9.06 in Scopus versus 4.03 in ESCI. The h-index was 29 in Scopus and 16 in ESCI. Several collaborative groups were identified at the national and international level. Conclusions: There is a progression of Insights into Imaging in the ranking of journals in the area that, if maintained, will allow it to remain in the first quartile in the Scopus database. The main topics of interest were technologies such as 'Magnetic resonance imaging', 'Computed tomography', 'Radiology', and 'Ultrasonography' and diseases such as 'Breast cancer' and those related to 'Paediatrics'. [ABSTRACT FROM AUTHOR]

Objectives: To analyse the coverage and main bibliometric indicators of Insights into Imaging in Scopus and the Emerging Sources Citation Index (ESCI) from the Web of Science Core Collection database. Methods: The evolution of journal production in the Scopus database was analysed according to document types, collaboration indexes between authors and institutions, and citation indicators (number of citations, SCImago Journal Rank, quartile, h-index, and most cited works). Networks of collaboration among authors, institutions, and countries were also analysed, as well as those of co-word networks. As a complementary source of information, the Emerging Source Citation Index from the Web of Science database was also considered. Results: Four hundred seventy-four papers were included in Scopus and 292 in ESCI. The index of collaboration was 5.18 for authors and 2.74 for institutions. International collaboration occurred in 24.7% of the papers. The number of citations received in Scopus (4295) exceeds the number of citations received in ESCI (1177). The average number of citations per paper was 9.06 in Scopus versus 4.03 in ESCI. The h-index was 29 in Scopus and 16 in ESCI. Several collaborative groups were identified at the national and international level. Conclusions: There is a progression of Insights into Imaging in the ranking of journals in the area that, if maintained, will allow it to remain in the first quartile in the Scopus database. The main topics of interest were technologies such as 'Magnetic resonance imaging', 'Computed tomography', 'Radiology', and 'Ultrasonography' and diseases such as 'Breast cancer' and those related to 'Paediatrics'. [ABSTRACT FROM AUTHOR]

This review presents basic information on the dosimetric quantities used in medical imaging for reporting patient doses and establishing diagnostic reference levels. The proper use of the radiation protection quantity "effective dose" to compare doses delivered by different radiological procedures and different imaging modalities with its uncertainties and limitations, is summarised. The estimates of population doses required by the European Directive on Basic Safety Standards is commented on. Referrers and radiologists should be familiar with the dose quantities to inform patients about radiation risks and benefits. The application of effective dose on the cumulative doses from recurrent imaging procedures is also discussed. Patient summary: Basic information on the measurement units (dosimetric quantities) used in medical imaging for reporting radiation doses should be understandable to patients. The Working Group on "Dosimetry for imaging in clinical practice" recommended that a brief explanation on the used dosimetric quantities and units included in the examination imaging report, should be available for patients. The use of the quantity "effective dose" to compare doses to which patients are exposed to from different radiological procedures and its uncertainties and limitations, should also be explained in plain language. This is also relevant for the dialog on to the cumulative doses from recurrent imaging procedures. The paper summarises these concepts, including the need to estimate the population doses required by the European Directive on Basic Safety Standards. Referrers and radiologists should be familiar with the dose quantities to inform patients about radiation risks and benefits. [ABSTRACT FROM AUTHOR]

Over the last decade, a huge number of medical visual data are widely used for diagnose, treatment, and follow-up. Retrieving needed medical image(s) from a huge number of images is one of the most widely used features in medical information systems, especially in medical image search engines. Indexing as part of search engines (or information retrieval systems), increases the speed (efficiency) of search and the information retrieval process. In this paper, a multidimensional indexing technique for medical images is presented that can improve effectiveness and efficiency of medical image search engines. The structure of the proposed multi-dimensional indexing technique and its main operations (i.e., creation, insertion, deletion and search) is designed and evaluated. In order to create this multidimensional index, the "vertical fragmentation" approach (which is usually applied for distributed database design) is used to determine the each of dimensions; roughly speaking, dimensions are different aspects of the medical images for a/some information need (e.g., image type and format, type of disorder, etc.). Accordingly, data structure of the proposed multidimensional indexing technique (which is named "Diamond") is formed by using the vertical fragmentation of medical image attributes (to differentiate the dimensions), and then using agglomerative hierarchical clustering to build up the hierarchy in each dimension. The proposed multi-dimensional indexing technique is implemented using the open-source search engine Lucene and compared with the built-in indexing technique available in the Lucene search engine, and also with the Terrier Platform (available for the benchmarking of information retrieval systems). In this evaluation, efficiency and effectiveness measures of the proposed multidimensional indexing technique (Diamond) are evaluated experimentally, beside the analysis of the designed data structure and its operations. For the experimental evaluation data set, images from Tabriz Behbood Hospital and a subset of TCIA images were used. Experimental evaluation results show that Diamond, the proposed multidimensional indexing technique improves both efficiency and effectiveness for a medical image search engine. [ABSTRACT FROM AUTHOR]

In disease diagnosis, medical image plays an important part. Its lossless compression is pretty critical, which directly determines the requirement of local storage space and communication bandwidth of remote medical systems, so as to help the diagnosis and treatment of patients. There are two extraordinary properties related to medical images: lossless and similarity. How to take advantage of these two properties to reduce the information needed to represent an image is the key point of compression. In this paper, we employ the big data mining to set up the image codebook. That is, to find the basic components of images. We propose a soft compression algorithm for multi-component medical images, which can exactly reflect the fundamental structure of images. A general representation framework for image compression is also put forward and the results indicate that our developed soft compression algorithm can outperform the popular benchmarks PNG and JPEG2000 in terms of compression ratio. [ABSTRACT FROM AUTHOR]

Medical image segmentation of tissue abnormalities, key organs, or blood vascular system is of great significance for any computerized diagnostic system. However, automatic segmentation in medical image analysis is a challenging task since it requires sophisticated knowledge of the target organ anatomy. This paper develops an end-to-end deep learning segmentation method called Contextual Multi-Scale Multi-Level Network (CMM-Net). The main idea is to fuse the global contextual features of multiple spatial scales at every contracting convolutional network level in the U-Net. Also, we re-exploit the dilated convolution module that enables an expansion of the receptive field with different rates depending on the size of feature maps throughout the networks. In addition, an augmented testing scheme referred to as Inversion Recovery (IR) which uses logical "OR" and "AND" operators is developed. The proposed segmentation network is evaluated on three medical imaging datasets, namely ISIC 2017 for skin lesions segmentation from dermoscopy images, DRIVE for retinal blood vessels segmentation from fundus images, and BraTS 2018 for brain gliomas segmentation from MR scans. The experimental results showed superior state-of-the-art performance with overall dice similarity coefficients of 85.78%, 80.27%, and 88.96% on the segmentation of skin lesions, retinal blood vessels, and brain tumors, respectively. The proposed CMM-Net is inherently general and could be efficiently applied as a robust tool for various medical image segmentations. [ABSTRACT FROM AUTHOR]

Interpretation differences between radiologists and diagnostic errors are significant issues in daily radiology practice. An awareness of errors and their underlying causes can potentially increase the diagnostic performance and reduce individual harm. The aim of this paper is to review both the classification of errors and the underlying biases. Case-based examples are presented and discussed for each type of error and bias to provide greater clarity and understanding. [ABSTRACT FROM AUTHOR]

This discussion paper has been produced within the context of the European Society of Radiology EuroSafe Imaging initiative and considers primarily the issues and challenges associated with justification of medical exposures using ionising radiation for individual patient diagnostic imaging procedures. It addresses both regulatory requirements and practical considerations and discusses approaches that are intended to improve justification. [ABSTRACT FROM AUTHOR]