Post-revascularization Ejection Fraction Prediction for Patients Undergoing Percutaneous Coronary Intervention Based on Myocardial Perfusion SPECT Imaging Radiomics: a Preliminary Machine Learning Study.

In this study, the ability of radiomics features extracted from myocardial perfusion imaging with SPECT (MPI-SPECT) was investigated for the prediction of ejection fraction (EF) post-percutaneous coronary intervention (PCI) treatment. A total of 52 patients who had undergone pre-PCI MPI-SPECT were enrolled in this study. After normalization of the images, features were extracted from the left ventricle, initially automatically segmented by k-means and active contour methods, and finally edited and approved by an expert radiologist. More than 1700 2D and 3D radiomics features were extracted from each patient's scan. A cross-combination of three feature selections and seven classifier methods was implemented. Three classes of no or dis-improvement (class 1), improved EF from 0 to 5% (class 2), and improved EF over 5% (class 3) were predicted by using tenfold cross-validation. Lastly, the models were evaluated based on accuracy, AUC, sensitivity, specificity, precision, and F-score. Neighborhood component analysis (NCA) selected the most predictive feature signatures, including Gabor, first-order, and NGTDM features. Among the classifiers, the best performance was achieved by the fine KNN classifier, which yielded mean accuracy, AUC, sensitivity, specificity, precision, and F-score of 0.84, 0.83, 0.75, 0.87, 0.78, and 0.76, respectively, in 100 iterations of classification, within the 52 patients with 10-fold cross-validation. The MPI-SPECT-based radiomic features are well suited for predicting post-revascularization EF and therefore provide a helpful approach for deciding on the most appropriate treatment.

Dental stem cell banking: Techniques and protocols.

Dental tissue-derived stem cells (DSCs) provide an easy, accessible, relatively noninvasive promising source of adult stem cells (ASCs), which brought encouraging prospective for their clinical applications. DSCs provide a perfect opportunity to apply for a patient's own ASC, which poses a low risk of immune rejection. However, problems associated with the long-term culture of stem cells, including loss of proliferation and differentiation capacities, senescence, genetic instability, and the possibility of microbial contamination, make cell banking necessary. With the rapid development of advanced cryopreservation technology, various international DSC banks have been established for both research and clinical applications around the world. However, few studies have been published that provide step-by-step guidance on DSCs isolation and banking methods. The purpose of this review is to present protocols and technical details for all steps of cryopreserved DSCs, from donor selection, isolation, cryopreservation, to characterization and quality control. Here, the emphasis is on presenting practical principles in accordance with the available valid guidelines.

Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas.

Purpose To evaluate ability of radiomic (computer-extracted imaging) features to distinguish non-small cell lung cancer adenocarcinomas from granulomas at noncontrast CT. Materials and Methods For this retrospective study, screening or standard diagnostic noncontrast CT images were collected for 290 patients (mean age, 68 years; range, 18-92 years; 125 men [mean age, 67 years; range, 18-90 years] and 165 women [mean age, 68 years; range, 33-92 years]) from two institutions between 2007 and 2013. Histopathologic analysis was available for one nodule per patient. Corresponding nodule of interest was identified on axial CT images by a radiologist with manual annotation. Nodule shape, wavelet (Gabor), and texture-based (Haralick and Laws energy) features were extracted from intra- and perinodular regions. Features were pruned to train machine learning classifiers with 145 patients. In a test set of 145 patients, classifier results were compared against a convolutional neural network (CNN) and diagnostic readings of two radiologists. Results Support vector machine classifier with intranodular radiomic features achieved an area under the receiver operating characteristic curve (AUC) of 0.75 on the test set. Combining radiomics of intranodular with perinodular regions improved the AUC to 0.80. On the same test set, CNN resulted in an AUC of 0.76. Radiologist readers achieved AUCs of 0.61 and 0.60, respectively. Conclusion Radiomic features from intranodular and perinodular regions of nodules can distinguish non-small cell lung cancer adenocarcinomas from benign granulomas at noncontrast CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Nishino in this issue.

Evaluation of bone regeneration potential of dental follicle stem cells for treatment of craniofacial defects.

BACKGROUND AIMS: Stem cell-based tissue regeneration offers potential for treatment of craniofacial bone defects. The dental follicle, a loose connective tissue surrounding the unerupted tooth, has been shown to contain progenitor/stem cells. Dental follicle stem cells (DFSCs) have strong osteogenesis capability, which makes them suitable for repairing skeletal defects. The objective of this study was to evaluate bone regeneration capability of DFSCs loaded into polycaprolactone (PCL) scaffold for treatment of craniofacial defects. METHODS: DFSCs were isolated from the first mandibular molars of postnatal Sprague-Dawley rats and seeded into the PCL scaffold. Cell attachment and cell viability on the scaffold were examined with the use of scanning electron microscopy and alamar blue reduction assay. For in vivo transplantation, critical-size defects were created on the skulls of 5-month-old immunocompetent rats, and the cell-scaffold constructs were transplanted into the defects. RESULTS: Skulls were collected at 4 and 8 weeks after transplantation, and bone regeneration in the defects was evaluated with the use of micro-computed tomography and histological analysis. Scanning electron microscopy and Alamar blue assay demonstrated attachment and proliferation of DFSCs in the PCL scaffold. Bone regeneration was observed in the defects treated with DFSC transplantation but not in the controls without DFSC transplant. Transplanting DFSC-PCL with or without osteogenic induction before transplantation achieved approximately 50% bone regeneration at 8 weeks. Formation of woven bone was observed in the DFSC-PCL treatment group. Similar results were seen when osteogenic-induced DFSC-PCL was transplanted to the critical-size defects. CONCLUSIONS: This study demonstrated that transplantation of DFSCs seeded into PCL scaffolds can be used to repair craniofacial defects.

Early Detection of Avian Diseases Based on Thermography and Artificial Intelligence.

Non-invasive measures have a critical role in precision livestock and poultry farming as they can reduce animal stress and provide continuous monitoring. Animal activity can reflect physical and mental states as well as health conditions. If any problems are detected, an early warning will be provided for necessary actions. The objective of this study was to identify avian diseases by using thermal-image processing and machine learning. Four groups of 14-day-old Ross 308 Broilers (20 birds per group) were used. Two groups were infected with one of the following diseases: Newcastle Disease (ND) and Avian Influenza (AI), and the other two were considered control groups. Thermal images were captured every 8 h and processed with MATLAB. After de-noising and removing the background, 23 statistical features were extracted, and the best features were selected using the improved distance evaluation method. Support vector machine (SVM) and artificial neural networks (ANN) were developed as classifiers. Results indicated that the former classifier outperformed the latter for disease classification. The Dempster-Shafer evidence theory was used as the data fusion stage if neither ANN nor SVM detected the diseases with acceptable accuracy. The final SVM-based framework achieved 97.2% and 100% accuracy for classifying AI and ND, respectively, within 24 h after virus infection. The proposed method is an innovative procedure for the timely identification of avian diseases to support early intervention.

Molecular Communication Transmitter Design in Limited-Capacity Storage Regime.

The limited storage capacity at the transmitters of a molecular communication (MC) system can affect the system's performance. One of the reasons for this limitation is the size restriction of the transmitter, which the storage must be replenished so that the transmitter has enough molecules for future transmission. This paper proposes a biologically inspired transmitter model based on neurons for MC whose storage charging and discharging follow differential equations. The proposed transmitter opens its outlet for a specific time in each time frame to exponentially release a portion of stored molecules to code bit-1 and remains silent to code bit-0. We analyze our model based on different transmission parameters. These parameters are the symbol duration, the release time duration, the storage capacity, and the release and replenishment rate of the storage. We find that the storage outlet must be open for a certain period within the time slot duration in order to improve the performance of the proposed system. Additionally, we demonstrate that determining the effect of storage capacity size can be important for practical MC due to the significant differences between the ideal transmitter and the proposed one, which have a limited size. We show that increases in the transmitter storage size can improve the system performance. As a result, taking a closer look at these practical transmitters is essential to solving the problems and challenges of molecular communication systems.

Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).

ATLAS: An Adaptive Transfer Learning Based Pain Assessment System: A Real Life Unsupervised Pain Assessment Solution.

Undertreatment or overtreatment of pain will cause severe consequences physiologically and psychologically. Thus, researchers have made great efforts to develop automatic pain assessment approaches based on physiological signals using machine learning techniques. However, state-of-art research mainly focuses on verifying the hypothesis that physiological signals can be used to assess pain. The critical assumption of these studies is that training data and testing data have the same distribution. However, this assumption may not hold in reallife scenarios, for instance, the adoption of machine learning model by a new patient. Such real-life scenarios in which user's data is unlabeled is largely neglected in literature. This study compensates for the rift by proposing an adaptive transfer learning based pain assessment system (ATLAS), a novel adaptive learning system based on the transfer learning algorithm Transfer Components Analysis (TCA) to minimize the distance between training data and unlabeled testing data. Experiments were conducted on BioVid database, and the results showed our approach outperforms three existing traditional machine learning-based approaches and achieves an accuracy just 2.0% below the accuracy with labeled data.

Convolution Neural Network for Pain Intensity Assessment from Facial Expression.

Pain is an unpleasant feeling that can reflect a patient's health situation. Since measuring pain is subjective, time-consuming, and needs continuous monitoring, automated pain intensity detection from facial expression holds great potential for smart healthcare applications. Convolutional Neural Networks (CNNs) are recently being used to identify features, map and model pain intensity from facial images, delivering great promise in helping practitioners detect disease. Limited research has been conducted to determine pain intensity levels across multiple classes. CNNs with simple learning schemes are limited in their ability to extract feature information from images. In order to develop a highly accurate pain intensity estimation system, this study proposes a Deep CNN (DCNN) model using the transfer learning technique, where a pre-trained DCNN model is adopted by replacing its dense upper layers, and the model is tuned using painful facial. We conducted experiments on the UNBC-McMaster shoulder pain archive database to estimate pain intensity in terms of seven-level thresholds using a given facial expression image. The experiments show our method achieves a promising improvement in terms of accuracy and performance to estimate pain intensity and outperform the-state-of-the-arts models.

Distinguishing Adenocarcinomas from Granulomas in the CT scan of the chest: performance degradation evaluation in the automatic segmentation framework.

OBJECTIVE: The most common histopathologic malignant and benign nodules are Adenocarcinoma and Granuloma, respectively, which have different standards of care. In this paper, we propose an automatic framework for the diagnosis of the Adenocarcinomas and the Granulomas in the CT scans of the chest from a private dataset. We use the radiomic features of the nodules and the attached vessel tortuosity for the diagnosis. The private dataset includes 22 CTs for each nodule type, i.e., adenocarcinoma and granuloma. The dataset contains the CTs of the non-smoker patients who are between 30 and 60 years old. To automatically segment the delineated nodule area and the attached vessels area, we apply a morphological-based approach. For distinguishing the malignancy of the segmented nodule, two texture features of the nodule, the curvature Mean and the number of the attached vessels are extracted. RESULTS: We compare our framework with the state-of-the-art feature selection methods for differentiating Adenocarcinomas from Granulomas. These methods employ only the shape features of the nodule, the texture features of the nodule, or the torsion features of the attached vessels along with the radiomic features of the nodule. The accuracy of our framework is improved by considering the four selected features.

Texture appearance model, a new model-based segmentation paradigm, application on the segmentation of lung nodule in the CT scan of the chest.

Lung cancer causes more than one million deaths worldwide each year. Averages of 5-year survival rate of patients with Non-small cell lung cancer (NSCLC), which is the most common type of lung cancer, is 15%. Computer-Aided Detection (CAD) is a very important tool for identifying lung lesions in medical imaging. In general, the process line of a CAD system can be divided into four main stages: preprocessing, localization, feature extraction, and classification. As segmentation is required for localization in computer vision and medical image analysis, this step has become a major and challenging problem, and much research has been done on new segmentation techniques. In recent years, interest in model-based segmentation methods has increased, and the reason for this is even if some object information is lost, such gaps can be filled by using the previous information in the model. This paper proposed Texture Appearance Model (TAM), which is a new model-based method and segments all types of nodule areas accurately and efficiently, including juxta-pleural nodules, without separating the lung from the surrounding area in a CT scan of the lung. In this method, Texture Representation of Image (TRI) is obtained using tissue texture feature extraction and feature selection algorithms. The proposed method has been evaluated in 85 nodules of the dataset, received from the Iranian hospital, in which the ground-truth annotation by physicians and CT imaging data were provided. The results show that the proposed algorithm has an encouraging performance for distinguishing different types of nodules, including pleural, cavity and non-solid nodules, achieving an average dice similarity coefficient (DSC) of 84.75%.

Motion-compensated noninvasive periodontal health monitoring using handheld and motor-based photoacoustic-ultrasound imaging systems.

Simultaneous visualization of the teeth and periodontium is of significant clinical interest for image-based monitoring of periodontal health. We recently reported the application of a dual-modality photoacoustic-ultrasound (PA-US) imaging system for resolving periodontal anatomy and periodontal pocket depths in humans. This work utilized a linear array transducer attached to a stepper motor to generate 3D images via maximum intensity projection. This prior work also used a medical head immobilizer to reduce artifacts during volume rendering caused by motion from the subject (e.g., breathing, minor head movements). However, this solution does not completely eliminate motion artifacts while also complicating the imaging procedure and causing patient discomfort. To address this issue, we report the implementation of an image registration technique to correctly align B-mode PA-US images and generate artifact-free 2D cross-sections. Application of the deshaking technique to PA phantoms revealed 80% similarity to the ground truth when shaking was intentionally applied during stepper motor scans. Images from handheld sweeps could also be deshaken using an LED PA-US scanner. In ex vivo porcine mandibles, pigmentation of the enamel was well-estimated within 0.1 mm error. The pocket depth measured in a healthy human subject was also in good agreement with our prior study. This report demonstrates that a modality-independent registration technique can be applied to clinically relevant PA-US scans of the periodontium to reduce operator burden of skill and subject discomfort while showing potential for handheld clinical periodontal imaging.

Automatic segmentation of Sperm's parts in microscopic images of human semen smears using concatenated learning approaches.

Accurate segmentation of the sperms in microscopic semen smear images is a prerequisite step in automatic sperm morphology analysis. It is a challenging task due to the non-uniform distribution of light in semen smear images, low contrast between sperm's tail and its surrounding region, the existence of various artifacts, high concentration of sperms and wide spectrum of the shapes of the sperm's parts. This paper proposes an automatic framework based on concatenated learning approaches to segment the external and internal parts of the sperms. The external parts of the sperms are segmented using two convolutional neural network (CNN) models which produce the probability maps of the head and the axial filament regions. To obtain acrosome and nucleus segments, the K-means clustering approach is applied to the head segments. A Support Vector Machine (SVM) classifier is used to classify each pixel of the axial filament segments to extract tail and mid-piece regions from obtained segments. The proposed method is validated on the images of the Gold-standard dataset. It achieves 0.90, 0.77, 0.77, 0.78, 0.75 and 0.64 of the average of dice similarity coefficient for the head, axial filament, acrosome, nucleus, tail, and mid-piece segments, respectively. Experimental results demonstrate that the proposed method outperforms state-of-the-art algorithms for the head and its internal parts segmentation. It also segments the axial filament region and its internal parts with desirable accuracy. Different from previous works, the proposed method is able to segment all parts of the sperms which enables automatic quantitative analysis of the sperm morphology.

Sparsity-based beamforming to enhance two-dimensional linear-array photoacoustic tomography.

In linear-array photoacoustic imaging (PAI), beamforming methods can be used to reconstruct the images. Delay-and-sum (DAS) beamformer is extensively used due to its simple implementation. However, this algorithm results in high level of sidelobes and low resolution. In this paper, it is proposed to form the photoacoustic (PA) images through a regularized inverse problem to address these limitations and improve the image quality. We define a forward/backward problem of the beamforming and solve the inverse problem using a sparse constraint added to the model which forces the sparsity of the output beamformed data. It is shown that the proposed Sparse beamforming (SB) method is robust against noise due to the sparsity nature of the problem. Numerical results show that the SB method improves the signal-to-noise ratio (SNR) for about 98.69 dB, 82.26 dB and 74.73 dB, in average, compared to DAS, delay-multiply-and-sum (DMAS) and double stage-DMAS (DS-DMAS), respectively. Also, quantitative evaluation of the experimental results shows a significant noise reduction using SB algorithm. In particular, the contrast ratio of the wire phantom at the depth of 30 mm is improved about 103.97 dB, 82.16 dB and 65.77 dB compared to DAS, DMAS and DS-DMAS algorithms, respectively, indicating a better performance of the proposed SB in terms of noise reduction.

Validation of delay-multiply-and-standard-deviation weighting factor for improved photoacoustic imaging of sentinel lymph node.

Delay-and-sum (DAS) is one of the most common algorithms used to construct the photoacoustic images due to its low complexity. However, it results in images with high sidelobes and low resolution. Delay-and-standard-deviation (DASD) weighting factor can improve the contrast of the images compared to DAS. However, it still suffers from high sidelobes. In this work, a new weighting factor, named delay-multiply-and-standard-deviation (DMASD) is introduced to enhance the contrast of the reconstructed images compared to other mentioned methods. In the proposed method, the SD of the mutual multiplied delayed signals are calculated, normalized and multiplied to DAS beamformed data. The results show that DMASD improves the signal-to-noise-ratio about 19.29 and 7.3 dB compared to DAS and DASD, respectively, for in vivo imaging of the sentinel lymph node. Moreover, the contrast ratio is improved by the DMASD about 23.61 and 10.81 dB compared to DAS and DASD, respectively.

Author Correction: Quantitative vessel tortuosity: A potential CT imaging biomarker for distinguishing lung granulomas from adenocarcinomas.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Photoacoustic image formation based on sparse regularization of minimum variance beamformer.

Delay-and-sum (DAS) is the most common algorithm used in photoacoustic (PA) image formation. However, this algorithm results in a reconstructed image with a wide mainlobe and high level of sidelobes. Minimum variance (MV), as an adaptive beamformer, overcomes these limitations and improves the image resolution and contrast. In this paper, a novel algorithm, named Modified-Sparse-MV (MS-MV), is proposed in which a ℓ 1-norm constraint is added to the MV minimization problem after some modifications, in order to suppress the sidelobes more efficiently, compared to MV. The added constraint can be interpreted as the sparsity of the output of the MV beamformed signals. Since the final minimization problem is convex, it can be solved efficiently using a simple iterative algorithm. The numerical results show that the proposed method, MS-MV beamformer, improves the signal-to-noise (SNR) about 19.48 dB, in average, compared to MV. Also, the experimental results, using a wire-target phantom, show that MS-MV leads to SNR improvement of about 2.64 dB in comparison with the MV.

Double-Stage Delay Multiply and Sum Beamforming Algorithm Applied to Ultrasound Medical Imaging.

In ultrasound (US) imaging, delay and sum (DAS) is the most common beamformer, but it leads to low-quality images. Delay multiply and sum (DMAS) was introduced to address this problem. However, the reconstructed images using DMAS still suffer from the level of side lobes and low noise suppression. Here, a novel beamforming algorithm is introduced based on expansion of the DMAS formula. We found that there is a DAS algebra inside the expansion, and we proposed use of the DMAS instead of the DAS algebra. The introduced method, namely double-stage DMAS (DS-DMAS), is evaluated numerically and experimentally. The quantitative results indicate that DS-DMAS results in an approximately 25% lower level of side lobes compared with DMAS. Moreover, the introduced method leads to 23%, 22% and 43% improvement in signal-to-noise ratio, full width at half-maximum and contrast ratio, respectively, compared with the DMAS beamformer.

Linear-array photoacoustic imaging using minimum variance-based delay multiply and sum adaptive beamforming algorithm.

In photoacoustic imaging, delay-and-sum (DAS) beamformer is a common beamforming algorithm having a simple implementation. However, it results in a poor resolution and high sidelobes. To address these challenges, a new algorithm namely delay-multiply-and-sum (DMAS) was introduced having lower sidelobes compared to DAS. To improve the resolution of DMAS, a beamformer is introduced using minimum variance (MV) adaptive beamforming combined with DMAS, so-called minimum variance-based DMAS (MVB-DMAS). It is shown that expanding the DMAS equation results in multiple terms representing a DAS algebra. It is proposed to use the MV adaptive beamformer instead of the existing DAS. MVB-DMAS is evaluated numerically and experimentally. In particular, at the depth of 45 mm MVB-DMAS results in about 31, 18, and 8 dB sidelobes reduction compared to DAS, MV, and DMAS, respectively. The quantitative results of the simulations show that MVB-DMAS leads to improvement in full-width-half-maximum about 96%, 94%, and 45% and signal-to-noise ratio about 89%, 15%, and 35% compared to DAS, DMAS, MV, respectively. In particular, at the depth of 33 mm of the experimental images, MVB-DMAS results in about 20 dB sidelobes reduction in comparison with other beamformers.

Double-Stage Delay Multiply and Sum Beamforming Algorithm: Application to Linear-Array Photoacoustic Imaging.

Photoacoustic imaging (PAI) is an emerging medical imaging modality capable of providing high spatial resolution of Ultrasound (US) imaging and high contrast of optical imaging. Delay-and-Sum (DAS) is the most common beamforming algorithm in PAI. However, using DAS beamformer leads to low resolution images and considerable contribution of off-axis signals. A new paradigm namely delay-multiply-and-sum (DMAS), which was originally used as a reconstruction algorithm in confocal microwave imaging, was introduced to overcome the challenges in DAS. DMAS was used in PAI systems and it was shown that this algorithm results in resolution improvement and sidelobe degrading. However, DMAS is still sensitive to high levels of noise, and resolution improvement is not satisfying. Here, we propose a novel algorithm based on DAS algebra inside DMAS formula expansion, double stage DMAS (DS-DMAS), which improves the image resolution and levels of sidelobe, and is much less sensitive to high level of noise compared to DMAS. The performance of DS-DMAS algorithm is evaluated numerically and experimentally. The resulted images are evaluated qualitatively and quantitatively using established quality metrics including signal-to-noise ratio (SNR), full-width-half-maximum (FWHM) and contrast ratio (CR). It is shown that DS-DMAS outperforms DAS and DMAS at the expense of higher computational load. DS-DMAS reduces the lateral valley for about 15 dB and improves the SNR and FWHM better than 13% and 30%, respectively. Moreover, the levels of sidelobe are reduced for about 10 dB in comparison with those in DMAS.