Current Medical Imaging - Volume 16, Issue 5, 2020

Background: According to the Standards for Reporting Vascular Changes on Neuroimaging, White Matter Hyperintensities (WMHs) are cerebral white matter lesions that are characterized by abnormal tissues of variable sizes and appear hyperintense in T2-weighted Magnetic Resonance (MR) measurements without cavitation (i.e., their tissue signals differ from those of Cerebrospinal Fluid or CSF). Such abnormal tissue regions are typically observed in the MR images of brains of healthy older adults and are associated with a number of geriatric neurodegenerative diseases. Explanations of the exact causes and mechanisms of these diseases remain inconclusive. Moreover, WMHs are typically identified by visual assessment and manual examination, both of which require considerable time. This brings up a need of developing a method for detecting WMHs more objectively and enabling patients to be treated early. As a consequence, damages on nerve cells can be limited and the severity of patients’ conditions can be contained. Aims: This paper presents a computer-aided technique for automatically detecting and segmenting anomalies in MR images. Methods: The method has two steps: (1) a Band Expansion Process (BEP) to expand the dimensions of brain MR images nonlinearly and (2) anomaly detection algorithms to detect WMHs. Synthesized MR images provided by BrainWeb were used as benchmarks against which the detection performance of the algorithms was determined. Results: The most notable findings are as follows: Firstly, compared with the other anomaly detection algorithms and the Lesion Segmentation Tool (LST), BEP-anomaly detection is shown to be the most effective in detecting WMHs. Secondly, across all levels of background noise and inhomogeneity, the mean Similarity Index (SI) produced by our proposed algorithm is higher than that produced by LST, indicating that the algorithm is more effective than LST in segmenting WMHs from brain MR images. Conclusion: Experimental results demonstrated a significantly high accuracy of the BEP-K/R-RX method in detection of synthetic brain MS lesion data. In the meantime, it also effectively enhances the detection of brain lesions.

Aims: The purpose of this study was to classify complicated uterine movements obtained by MRI scanner and investigate the relationship between uterine peristalsis and female infertility. Methods: Uterine movements are classified into six fundamental movements by their motility form and directions. Computer simulation of the uterine movements is performed. Results: Comparison results between the real MRI images and the simulated images showed that any five in our dataset uterine movement was successfully reproduced by a combination of these six fundamental movements. The point and surface vibration model appropriately mimicked the movements with the propagation velocity of 0.68 [mm/sec]. Conclusion: By analyzing six fundamental movements using data from 26 MRI scans, it was found that two fundamental movements were identified as candidate factors for female infertility.

Background: Anterior cruciate ligament (ACL) injury causes knee instability which affects sports activity involving cutting and twisting motions. The ACL reconstruction surgery replaces the damaged ACL with artificial one which is fixed to the bone tunnels opened by the surgeon. The outcome of the ACL reconstruction is strongly related to the placement of the bone tunnels, therefore, the optimization of tunnel drilling technique is an important factor to obtain satisfactory surgical results. Aims: The quadrant method is used for the post-operative evaluation of the ACL reconstruction surgery, which evaluates the bone tunnel opening sites on the lateral 2D X-ray radiograph. Methods: For the purpose of applying the quadrant method to the pre-operative knee MRI, we have synthesized the pseudo lateral 2D X-ray radiograph from the patients' knee MRI. This paper proposes a computer-aided surgical planning system for the ACL reconstruction. The proposed system estimates appropriate bone tunnel opening sites on the pseudo lateral 2D X-ray radiograph synthesized from the pre-operative knee MRI. Results: In the experiment, the proposed method was applied to 98 subjects including subjects with osteoarthritis. The experimental results showed that the proposed method can estimate the bone tunnel opening sites accurately. The other experiment using 36 healthy patients showed that the proposed method is robust to the knee shape deformation caused by disease. Conclusion: It is verified that the proposed method can be applied to subjects with osteoarthritis.

Background: This study presents a novel method of constructing a spatiotemporal statistical shape model (st-SSM) for adult brain. St-SSM is an extension of statistical shape model (SSM) in the temporal domain which will represent the statistical variability of shape as well as the temporal change of statistical variance with respect to time. Aims: Expectation-Maximization (EM) based weighted principal component analysis (WPCA) using a temporal weight function is applied where the eigenvalues of each data are estimated by Estep using temporal eigenvectors, and M-step updates Eigenvectors in order to maximize the variance. Both E and M-step are iterated until updating vectors reaches the convergence point. A weight parameter for each subject is allocated in accordance with the subject’s age to calculate the weighted variance. A Gaussian function is utilized to define the weight function. The center of the function is a time point while the variance is a predefined parameter. Methods: The proposed method constructs adult brain st-SSM by changing the time point between minimum to maximum age range with a small interval. Here, the eigenvectors changes with aging. The feature vector of representing adult brain shape is extracted through a level set algorithm. To validate the method, this study employed 103 adult subjects (age: 22 to 93 y.o. with Mean ± SD = 59.32±16.89) from OASIS database. st-SSM was constructed for time point 40 to 90 with a step of 2. Results: We calculated the temporal deformation change between two-time points and evaluated the corresponding difference to investigate the influence of analysis parameter. An application of the proposed model is also introduced which involves Alzheimer’s disease (AD) identification utilizing support vector machine. Conclusion: In this study, st-SSM based adult brain shape feature extraction and classification techniques are introduced to classify between normal and AD subject as an application.

Background: Bone metastasis is common in cancer. Evaluating the metastatic status in cancer is of utmost importance in order to provide the best patient’s management. Discussion: Bone scintigraphy is widely used for evaluation of bone metastasis. It has high sensitivity with limited specificity. Planar bone scintigraphy has been shown to have increased radiotracer uptake without accurate anatomic localization and characterization. Hybrid Single-Photon Emission Computed Tomography/Computerized Tomography (SPECT/CT) system has been developed by combination of SPECT and CT. Accurate lesion localization and discrimination of equivocal bone lesions is an advantage in this hybrid technique. It improves diagnostic accuracy by differentiation of benign bone lesions from malignant ones due to their morphological changes. So, SPECT/CT improves the specificity of bone scintigraphy leading to better outcomes in diagnosis and treatment outcomes of bone metastatic cancer patients. Conclusion: In here, we discussed the prognostic value of bone scintigraphy and SPECT/CT in bone metastasis with our clinical experience and review of the literature.

Background: Automated intelligent systems for unbiased diagnosis are primary requirement for the pigment lesion analysis. It has gained the attention of researchers in the last few decades. These systems involve multiple phases such as pre-processing, feature extraction, segmentation, classification and post processing. It is crucial to accurately localize and segment the skin lesion. It is observed that recent enhancements in machine learning algorithms and dermoscopic techniques reduced the misclassification rate therefore, the focus towards computer aided systems increased exponentially in recent years. Computer aided diagnostic systems are reliable source for dermatologists to analyze the type of cancer, but it is widely acknowledged that even higher accuracy is needed for computer aided diagnostic systems to be adopted practically in the diagnostic process of life threatening diseases. Introduction: Skin cancer is one of the most threatening cancers. It occurs by the abnormal multiplication of cells. The core three types of skin cells are: Squamous, Basal and Melanocytes. There are two wide classes of skin cancer; Melanocytic and non-Melanocytic. It is difficult to differentiate between benign and malignant melanoma, therefore dermatologists sometimes misclassify the benign and malignant melanoma. Melanoma is estimated as 19th most frequent cancer, it is riskier than the Basel and Squamous carcinoma because it rapidly spreads throughout the body. Hence, to lower the death risk, it is critical to diagnose the correct type of cancer in early rudimentary phases. It can occur on any part of body, but it has higher probability to occur on chest, back and legs. Methods: The paper presents a review of segmentation and classification techniques for skin lesion detection. Dermoscopy and its features are discussed briefly. After that Image pre-processing techniques are described. A thorough review of segmentation and classification phases of skin lesion detection using deep learning techniques is presented Literature is discussed and a comparative analysis of discussed methods is presented. Conclusion: In this paper, we have presented the survey of more than 100 papers and comparative analysis of state of the art techniques, model and methodologies. Malignant melanoma is one of the most threating and deadliest cancers. Since the last few decades, researchers are putting extra attention and effort in accurate diagnosis of melanoma. The main challenges of dermoscopic skin lesion images are: low contrasts, multiple lesions, irregular and fuzzy borders, blood vessels, regression, hairs, bubbles, variegated coloring and other kinds of distortions. The lack of large training dataset makes these problems even more challenging. Due to recent advancement in the paradigm of deep learning, and specially the outstanding performance in medical imaging, it has become important to review the deep learning algorithms performance in skin lesion segmentation. Here, we have discussed the results of different techniques on the basis of different evaluation parameters such as Jaccard coefficient, sensitivity, specificity and accuracy. And the paper listed down the major achievements in this domain with the detailed discussion of the techniques. In future, it is expected to improve results by utilizing the capabilities of deep learning frameworks with other pre and post processing techniques so reliable and accurate diagnostic systems can be built.

Cardiovascular death is one of the leading causes worldwide; an accurate identification followed by diagnosing the cardiovascular disease increases the chance of a better recovery. Among different demonstrated strategies, imaging on cardiac infections yields a visible result and highly reliable compared to other analytical methods. Two-dimensional spot tracking imaging is the emerging new technology that has been used to study the function and structure of the heart and test the deformation and movement of the myocardium. Particularly, it helps to capture the images of each segment in different directions of myocardial strain values, such as valves of radial strain, longitudinal strain, and circumferential strain. In this overview, we discussed the imaging of infections in the heart by using the two-dimensional spot tracking.

Liver is the predominant site of metastatization for neuroendocrine tumors (NETs). Up to 75% of patients affected by intestinal NETs present liver metastases at diagnosis. For hepatic NET, surgery represents the most effective approach but is often unfeasible due to the massive involvement of multifocal disease. In such cases, chemotherapy, peptide receptor radionuclide therapy and loco-regional treatments may represent alternative therapeutic options. In particular, radioembolization with 90Y-microspheres has been introduced as a novel technique for treating hepatic malignant lesions, combining the principles of embolization and radiation therapy. In order to evaluate the response to 90Y-radioembolization, standard radiologic criteria have been demonstrated to present several limitations. 18Fluoro-deoxyglucose (FDG) Positron Emission Tomography (PET) is routinely used for monitoring the response to therapy in oncology. Nevertheless, NETs often present low glycolytic activity thus the conventional 18FDG PET may not be adequate for these tumors. For many years, somatostatin receptor scintigraphy (SRS) with 111In-pentetreotide has been used for diagnosis and staging of NETs. More recently, three 68Ga-DOTA-compounds have been developed and introduced for the imaging of NETs with PET technology. The aim of the present paper was to review the existing literature concerning the application of different metabolic and molecular probes for the imaging evaluation of hepatic NETs following 90Y-RE.

Background: The cone-beam computed tomography (CBCT) technology has continuously evolved since its appearance in oral medicine in the early 2000s. Objectives: To present recent advances in CBCT in oral medicine: i) selection of recent and consensual evidence-based sources, ii) structured summary of the information based on an iterative framework and iii) compliance with ethical, public health and patient-centered concerns. Main Findings: We will focus on technological advances, such as sensors and reconstruction algorithms used to improve the constant quality of the image and dosimetry. CBCT examination is now performed in almost all disciplines of oral medicine: currently, the main clinical disciplines that use CBCT acquisitions are endodontics and oral surgery, with clearly defined indications. Periodontology and ear, nose and throat medicine are more recent fields of application. For a given application and indication, the smallest possible field of view must be used. One of the major challenges in contemporary healthcare is ensuring that technological developments do not take precedence over admitted standards of care. The entire volume should be reviewed in full, with a systematic approach. All findings are noted in the patient’s record and explained to the patient, including incidental findings. This presupposes the person reviewing the images is sufficiently trained to interpret such images, inform the patient and organize the clinical pathway, with referrals to other medical or oral medicine specialties as needed. Conclusion: A close collaboration between dentists, medical physicists, radiologists, radiographers and engineers is critical for all aspects of CBCT technology.

Prostate cancer is one of the most common cancers in men. This cancer is often associated with indolent tumors with little or no lethal potential. Some of the patients with aggressive prostate cancer have increased morbidity and early deaths. A major complication in advanced prostate cancer is bone metastasis that mainly results in pain, pathological fractures, and compression of spinal nerves. These complications in turn cause severe pain radiating to the extremities and possibly sensory as well as motor disturbances. Further, in patients with a high risk of metastases, treatment is limited to palliative therapies. Therefore, accurate methods for the detection of bone metastases are essential. Technical advances such as single-photon emission computed tomography/ computed tomography (SPECT/CT) have emerged after the introduction of bone scans. These advanced methods allow tomographic image acquisition and help in attenuation correction with anatomical co-localization. The use of positron emission tomography/CT (PET/CT) scanners is also on the rise. These PET scanners are mainly utilized with 18F-sodium-fluoride (NaF), in order to visualize the skeleton and possible changes. Moreover, NaF PET/CT is associated with higher tracer uptake, increased target-to-background ratio and has a higher spatial resolution. However, these newer technologies have not been adopted in clinical guidelines due to lack of definite evidence in support of their use in bone metastases cases. The present review article is focused on current perspectives and challenges of computerized tomography (CT) applications in cases of bone metastases during prostate cancer.

Background: Radiological imaging methods used at a large scale in the assessment of hepatic lesions include: Ultrasound, computed tomography and magnetic resonance. To further characterize these lesions, specific contrast agents may be added, thus revealing the vascularity of the lesions. Discussion: This review focuses on gadoxetic acid, which is a hepatospecific contrast agent used in MRI. The aim of the review is to briefly explain the mechanism of GA enhancement, describe the enhancement patterns of some benign and malignant hepatic lesions and discuss possible advantages of GA over standard contrast agents. Conclusion: The role of GA in functional MR cholangiography and the idea of accessing liver function by measuring parenchymal enhancement will also be explained.

Background: Shear wave electrography (SWE) is a novel non-invasive imaging technique which demonstrate tissue elasticity. Recent research evaluating the elasticity properties of normal and pathological tissues emphasize the diagnostic importance of this technique. Aims: Polycystic ovarian syndrome (PCOS), which is characterized by menstrual irregularity, hyperandrogenism, and polycystic overgrowth, may cause infertility. The aim of this study was to evaluate the elasticity of ovaries in patients with PCOS using SWE. Methods: 66 patients diagnosed with PCOS according to the Rotterdam criteria (PCOS = group I) and 72 patients with non-PCOS (Control = group II), were included in the study. Demographic and clinical characteristics of the participants were recorded. Ovarian elasticity was assessed in all patients with SWE, and speed values were obtained from the ovaries. The elasticity of the ovaries was compared between the two groups. Results: While there were statistically significant differences between the groups in body mass index (BMI), right and left ovarian volumes, luteinizing hormone and testosterone levels (p<0.05), no significant differences were found between groups I and II in the velocity (for the right ovary 3.89±1.81 vs. 2.93±0.72, p=0.301; for the left ovary 2.88±0.65 vs. 2.95±0.80, p=0.577) and elastography (for the right ovary 36.62±17.78 vs. 36.79±14.32, p=0.3952; for the left ovary 36.56±14.15 vs. 36.26±15.10, p=0.903) values, respectively. Conclusion: We could not obtain different velocity and elastography values from the ovaries of the patients with PCOS using SWE. Therefore, further large-scale studies are needed to elucidate this issue.

Background: Valvular heart disease is a serious disease leading to mortality and increasing medical care cost. The aortic valve is the most common valve affected by this disease. Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks (CNN) that can function optimally during a live echocardiographic examination for detection of the aortic valve. An automated detection system in an echocardiogram will improve the accuracy of medical diagnosis and can provide further medical analysis from the resulting detection. Methods: Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional based Convolutional Neural Network (R-CNN) with various feature extractors were trained on echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography videos. Results: Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper- second (fps) during real-time detection but managed to perform better than the other neural network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all models. Conclusion: Our findings provide a foundation for implementing a convolutional detection system to echocardiography for medical purposes.

Background: Low Back Pain (LBP) is a common disorder involving the muscles and bones and about half of the people experience LBP at some point of their lives. Since the social economic cost and the recurrence rate over the lifetime is very high, the treatment/rehabilitation of chronic LBP is important to physiotherapists, both for clinical and research purposes. Trunk muscles such as the lumbar multifidi is important in spinal functions and intramuscular fat is also important in understanding pain control and rehabilitations. However, the analysis of such muscles and related fat require many human interventions and thus suffers from the operator subjectivity especially when the ultrasonography is used due to its cost-effectiveness and no radioactive risk. Aims: In this paper, we propose a fully automatic computer vision based software to compute the thickness of the lumbar multifidi muscles and to analyze intramuscular fat distribution in that area. Methods: The proposed system applies various image processing algorithms to enhance the intensity contrast of the image and measure the thickness of the target muscle. Intermuscular fat analysis is done by Fuzzy C-Means (FCM) clustering based quantization. Results: In experiment using 50 DICOM format ultrasound images from 50 subjects, the proposed system shows very promising result in computing the thickness of lumbar multifidi. Conclusion: The proposed system have minimal discrepancy(less than 0.2 cm) from human expert for 72% (36 out of 50 cases) of the given data. Also, FCM based intramuscular fat analysis looks better than conventional histogram analysis.

Background: Echolocation is a technique whereby the location of objects is determined via reflected sound. Currently, some visually impaired individuals use a form of echolocation to locate objects and to orient themselves. However, this method takes years of practice to accurately utilize. Aims: This paper presents the development of a sensory substitution device for visually impaired users, which gauged distances and the placement of objects. Methods: Using ultrasonic technology, the device employed a method of echolocation to increase the user's independence and mobility. The main components of this device are an ultrasound transceiver and a miniaturized Arduino board. Through research and prototyping, this technology was integrated into a biomedical application in a watch form factor which provides feedback to the user regarding the measured distance by the ultrasonic transducer. Results: The output of this process is a tactile feedback that varies in intensity proportional to the distance of the detected object. We tested the device in different scenarios including different distances from a different material. The difference between the device reading and the actual distance, from 0 to 400 cm was statistically insignificant. Conclusion: It is believed this device will boost the confidence of the user in navigation.

Background: Accurate segmentation of Breast Infrared Thermography is an important step for early detection of breast pathological changes. Automatic segmentation of Breast Infrared Thermography is a very challenging task, as it is difficult to find an accurate breast contour and extract regions of interest from it. Although several semi-automatic methods have been proposed for segmentation, their performance often depends on hand-crafted image features, as well as preprocessing operations. Objectives: In this work, an approach to automatic semantic segmentation of the Breast Infrared Thermography is proposed based on end-to-end fully convolutional neural networks and without any pre or post-processing. Methods: The lack of labeled Breast Infrared Thermography data limits the complete utilization of fully convolutional neural networks. The proposed model overcomes this challenge by applying data augmentation and two-tier transfer learning from bigger datasets combined with adaptive multi-tier fine-tuning before training the fully convolutional neural networks model. Results: Experimental results show that the proposed approach achieves better segmentation results: 97.986% accuracy; 98.36% sensitivity and 97.61% specificity compared to hand-crafted segmentation methods. Conclusion: This work provided an end-to-end automatic semantic segmentation of Breast Infrared Thermography combined with fully convolutional networks, adaptive multi-tier fine-tuning and transfer learning. Also, this work was able to deal with challenges in applying convolutional neural networks on such data and achieving the state-of-the-art accuracy.

Introduction: Coexistance of pancreatic carcinoma and Leriche syndrome is an extremely rare pathological condition. Leriche syndrome is defined as occlusion of the distal aorta at the bifurcation into the common iliac arteries. Case Report: We report the case of a 57-year old male patient with a locally advanced pancreatic tumor that during chemotherapy presented Leriche syndrome. Four months after the diagnosis and although the initial staging by MRI had only revealed a few atheromatic lesions of the abdominal aorta, the patient complained about claudication of the legs and hypoesthesia. Angiography with multi-detector computed tomography (MDCTA) was performed using aortography protocol and three-dimensional reconstruction of the images followed, demonstrating the relationship between pancreatic carcinoma and Leriche syndrome. Conclusion: Review of the literature revealed that acute abdominal thrombosis is rare in cancer patients. To our knowledge, complete occlusion of the aorta in a patient with pancreatic cancer has not been reported yet.