Feature Preserving Autofocus Algorithm for Phase Error Correction of SAR Images.

Autofocus is an essential technique for airborne synthetic aperture radar (SAR) imaging to correct phase errors mainly due to unexpected motion error. There are several well-known conventional autofocus methods such as phase gradient autofocus (PGA) and minimum entropy (ME). Although these methods are still widely used for various SAR applications, each method has drawbacks such as limited bandwidth of estimation, low convergence rate, huge computation burden, etc. In this paper, feature preserving autofocus (FPA) algorithm is newly proposed. The algorithm is based on the minimization of the cost function containing a regularization term. The algorithm is designed for postprocessing purpose, which is different from the existing regularization-based algorithms such as sparsity-driven autofocus (SDA). This difference makes the proposed method far more straightforward and efficient than those existing algorithms. The experimental results show that the proposed algorithm achieves better performance, convergence, and robustness than the existing postprocessing autofocus algorithms.

Consensus for Thoracoscopic Left Upper Lobectomy-Essential Components and Targets for Simulation.

BACKGROUND: Simulation-based training is a valuable component of cardiothoracic surgical education. Effective curriculum development requires consensus on procedural components and focused attention on specific learning objectives. Through use of a Delphi process, we established consensus on the steps of video-assisted thoracoscopic surgery (VATS) left upper lobectomy and identified targets for simulation. METHODS: Experienced thoracic surgeons were randomly selected for participation. Surgeons voted and commented on the necessity of individual steps comprising VATS left upper lobectomy. Steps with greater than 80% of participants in agreement of their necessity were determined to have established "consensus." Participants voted on the physical or cognitive complexity of each, or both, and chose steps most amenable to focused simulation. RESULTS: Thirty thoracic surgeons responded and joined in the voting process. Twenty operative steps were identified, with surgeons reaching consensus on the necessity of 19. Components deemed most difficult and amenable to simulation included those related to dissection and division of the bronchus, artery, and vein. CONCLUSIONS: Through a Delphi process, surgeons with a variety of practice patterns can achieve consensus on the operative steps of left upper lobectomy and agreement on those most appropriate for simulation. This information can be implemented in the development of targeted simulation for VATS lobectomy.

Deep Residual CNN-Based Ocular Recognition Based on Rough Pupil Detection in the Images by NIR Camera Sensor.

Accurate segmentation of the iris area in input images has a significant effect on the accuracy of iris recognition and is a very important preprocessing step in the overall iris recognition process. In previous studies on iris recognition, however, the accuracy of iris segmentation was reduced when the images of captured irises were of low quality due to problems such as optical and motion blurring, thick eyelashes, and light reflected from eyeglasses. Deep learning-based iris segmentation has been proposed to improve accuracy, but its disadvantage is that it requires a long processing time. To resolve this problem, this study proposes a new method that quickly finds a rough iris box area without accurately segmenting the iris region in the input images and performs ocular recognition based on this. To address this problem of reduced accuracy, the recognition is performed using the ocular area, which is a little larger than the iris area, and a deep residual network (ResNet) is used to resolve the problem of reduced recognition rates due to misalignment between the enrolled and recognition iris images. Experiments were performed using three databases: Institute of Automation Chinese Academy of Sciences (CASIA)-Iris-Distance, CASIA-Iris-Lamp, and CASIA-Iris-Thousand. They confirmed that the method proposed in this study had a higher recognition accuracy than existing methods.

Invasive Mediastinal Staging for Lung Cancer by The Society of Thoracic Surgeons Database Participants.

BACKGROUND: Prior studies suggest underutilization of invasive mediastinal staging for lung cancer. We hypothesized that The Society of Thoracic Surgeons General Thoracic Surgery Database (STS-GTSD) participants would have higher rates of invasive staging compared with previous reports. METHODS: We conducted a retrospective cohort study (2012 to 2016) of lung cancer patients staged by computed tomography and positron-emission tomography and first treated with an anatomic resection. We defined invasive staging by the use of mediastinoscopy, endosonography, or thoracoscopy. Standardized incidence ratios were used to compare participant-level rates of invasive staging, and Poisson regression was used to identify factors associated with invasive staging. RESULTS: Among 29,015 patients across 256 participating STS-GTSD sites, 34% (95% confidence interval: 33% to 34%) underwent invasive staging. The overall rate of invasive staging did not change between 2012 and 2016 (p trend = 0.16). Increasing clinical stage and features suggestive of a central tumor were associated with invasive staging (p < 0.001). Rates of invasive staging among patients with clinical stage IB or greater or features suggestive of a central tumor were 43% (95% confidence interval: 42% to 44%) and 52% (95% confidence interval: 50% to 54%), respectively. There was a more than 40-fold variation in rates of invasive staging across 251 centers contributing at least 10 cases (standardized incidence ratio: lowest = 0.08; highest = 3.26); 66 sites (26%) performed invasive mediastinal staging less often than average and 77 sites (31%) performed invasive staging more often than average. CONCLUSIONS: The STS-GTSD participants performed invasive mediastinal staging more frequently than prior reports, and yet only in a minority of patients. Rates of invasive mediastinal staging vary widely across STS-GTSD participants.

Surgical quality of wedge resection affects overall survival in patients with early stage non-small cell lung cancer.

OBJECTIVES: Very few studies have examined the quality of wedge resection in patients with non-small cell lung cancer. Using the National Cancer Database, we evaluated whether the quality of wedge resection affects overall survival in patients with early disease and how these outcomes compare with those of patients who receive stereotactic radiation. METHODS: We identified 14,328 patients with cT1 to T2, N0, M0 disease treated with wedge resection (n = 10,032) or stereotactic radiation (n = 4296) from 2005 to 2013 and developed a subsample of propensity-matched wedge and radiation patients. Wedge quality was grouped as high (negative margins, >5 nodes), average (negative margins, ≤5 nodes), and poor (positive margins). Overall survival was compared between patients who received wedge resection of different quality and those who received radiation, adjusting for demographic and clinical variables. RESULTS: Among patients who underwent wedge resection, 94.6% had negative margins, 44.3% had 0 nodes examined, 17.1% had >5 examined, and 3.0% were nodally upstaged; 16.7% received a high-quality wedge, which was associated with a lower risk of death compared with average-quality resection (adjusted hazard ratio [aHR], 0.74; 95% confidence interval [CI], 0.67-0.82). Compared with stereotactic radiation, wedge patients with negative margins had significantly reduced hazard of death (>5 nodes: aHR, 0.50; 95% CI, 0.43-0.58; ≤5 nodes: aHR, 0.65; 95% CI, 0.60-0.70). There was no significant survival difference between margin-positive wedge and radiation. CONCLUSIONS: Lymph nodes examined and margins obtained are important quality metrics in wedge resection. A high-quality wedge appears to confer a significant survival advantage over lower-quality wedge and stereotactic radiation. A margin-positive wedge appears to offer no benefit compared with radiation.

Neoadjuvant Chemoradiation Shows No Survival Advantage to Chemotherapy Alone in Stage IIIA Patients.

BACKGROUND: For operable patients with clinical stage IIIA non-small cell lung cancer, the optimum neoadjuvant treatment strategy remains unclear. Our aim was to compare perioperative and long-term outcomes for patients receiving neoadjuvant chemoradiotherapy (NCRT) versus neoadjuvant chemotherapy (NCT) alone. METHODS: We queried the National Cancer Database to identify all patients with N2 and either T1-T2 non-small cell lung cancer who received either NCRT or NCT followed by lobectomy between 2006 and 2012. Patients with T3 tumors were excluded. A propensity match analysis was performed incorporating preoperative variables, and the incidence of postoperative complications, pathologic downstaging, and long-term survival were compared. RESULTS: In all, 1,936 patients met criteria, 745 NCT and 1,191 NCRT. The NCRT patients were younger, less likely to be treated at an academic medical center, and more likely to have adenocarcinoma. After propensity matching, patients in the NCT group showed lower 30-day mortality (1.3% versus 2.9%) and 90-day mortality (2.9% versus 6.0%), and were more likely to undergo a minimally invasive resection (25.7% versus 14.1%). The NCRT patients were more likely to have a pathologic complete response (14.2% versus 4.0%) and to be N0 at the time of resection (45.2% versus 38.7%). In the multivariable analysis, NCRT patients were at a greater risk of mortality than NCT patients (hazard ratio 1.18, 95% confidence interval: 1.03 to 1.36). CONCLUSIONS: In our cohort, combined neoadjuvant chemotherapy and radiation therapy was associated with improved pathologic downstaging but showed increased perioperative mortality with no improvement in long-term overall survival. For stage IIIA patients with smaller tumors without local invasion, chemotherapy alone may be the preferred neoadjuvant treatment.

Predictors of pathologic upstaging in early esophageal adenocarcinoma: Results from the national cancer database.

BACKGROUND: Upstaging in early esophageal adenocarcinoma (EAC) patients happens at a high rate and has implications for treatment. We sought to identify risk factors predicting upstaging. STUDY DESIGN: The National Cancer Database (2010-2013) was queried for all patients with clinical T1/T2 and N0 EAC who underwent esophagectomy without neoadjuvant therapy. Logistic regression models were developed to investigate risk factors for upstaging. RESULTS: A total of 1120 patients were included. Pathologic upstaging occurred in 21.3% (n = 239). After adjustment, risk of upstaging increased with tumor size (tumor size 1-3 cm, OR 4.57,95% CI 2.58-8.10, tumor size >3 cm, OR 10.57, 95% CI 5.77-19.35, as compared to tumors <1 cm) as well as with positive margins (OR 4.13, 95% CI 2.17-7.87) and > than 10 lymph nodes examined (OR 1.85, 95% CI 1.29-2.63), while facility volume was not significant. Odds of upstaging increased linearly with number of lymph nodes examined (OR 1.02 per node). CONCLUSION: Our data underscore the importance of tumor size as a predictor for upstaging and of completing a thorough lymph node dissection for staging purposes.

Improved Lymph Node Staging in Early-Stage Lung Cancer in the National Cancer Database.

BACKGROUND: Lymph node assessment for non-small cell lung cancer (NSCLC) shows wide variation among centers. Our aim was to assess the quality of lymph node assessment in early-stage NSCLC and determine whether any factors are associated with improved lymph node harvest. METHODS: We queried the National Cancer Database to identify patients with clinical stage I NSCLC who underwent segmentectomy or lobectomy between 2004 and 2013. Patients were stratified into three groups (≤5, 6 to 15, and >15) based on the number of lymph nodes assessed. RESULTS: Patients (n = 51,358) met criteria, and mean lymph nodes assessed increased from 8.1 to 10.0 (p < 0.001) over the study period. There was a significant decrease in the percentage of patients with 0 to 5 nodes assessed (41.1% versus 31.1%, p < 0.001) and a significant increase in patients with more than 15 nodes assessed (10.1% versus 17.0%, p < 0.001). Patients at academic centers were less likely to have only 0 to 5 nodes assessed (27.2% versus 43.6% for community, p < 0.001). Variables associated with more than 15 nodes assessed were increasing year, age older than 65 years, male sex, non-African American race, academic centers, lobectomy, and clinical T2 disease. Patients with more than 14 nodes assessed demonstrated more nodal upstaging (17.9% versus 10.9% for 1 to 14 nodes, p < 0.001). Multivariable analysis suggests that at least 14 nodes should be assessed to maximize the probability that node-positive patients are correctly identified. CONCLUSIONS: Lymph node assessment has improved since 2004 but varies by facility type and other characteristics. In our analysis removing at least 14 nodes was associated with more accurate staging.

Road Lane Detection Robust to Shadows Based on a Fuzzy System Using a Visible Light Camera Sensor.

Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.

Remote Marker-Based Tracking for UAV Landing Using Visible-Light Camera Sensor.

Unmanned aerial vehicles (UAVs), which are commonly known as drones, have proved to be useful not only on the battlefields where manned flight is considered too risky or difficult, but also in everyday life purposes such as surveillance, monitoring, rescue, unmanned cargo, aerial video, and photography. More advanced drones make use of global positioning system (GPS) receivers during the navigation and control loop which allows for smart GPS features of drone navigation. However, there are problems if the drones operate in heterogeneous areas with no GPS signal, so it is important to perform research into the development of UAVs with autonomous navigation and landing guidance using computer vision. In this research, we determined how to safely land a drone in the absence of GPS signals using our remote maker-based tracking algorithm based on the visible light camera sensor. The proposed method uses a unique marker designed as a tracking target during landing procedures. Experimental results show that our method significantly outperforms state-of-the-art object trackers in terms of both accuracy and processing time, and we perform test on an embedded system in various environments.

Usefulness of titanium mesh cage for posterior C1-C2 fixation in patients with atlantoaxial instability.

The aim of this study was to investigate the usefulness of titanium mesh cage as an interlaminar spacer combined with nitinol shape memory loop fixation in patients with atlantoaxial instability.From April 2009 to March 2017, among the patients with atlantoaxial instability, a total of 30 patients were treated by nitinol shape memory loop fixation combined with titanium mesh cage as a spacer. We retrospectively reviewed 30 enrolled patients. Successful fusion was determined as improvement of symptoms and radiologic findings of bone fusion. We also reviewed surgical complications, instrumentation failure, bony fusion rate, and posterior atlantodental interval (PADI).After surgery, the symptoms of all patients significantly improved. Successful fusion was documented throughout the follow-up period. Evidence of solid bridging bone was found, and no instability was seen on flexion-extension radiographs and callus formation on 3D cervical spine computed tomography (CT) 6 months postoperatively in all cases. No surgical complications were observed. No cases of instrumentation failure were observed. The mean PADI also improved significantly to 22.45 ±â€Š1.11 mm 6 months postoperatively compared with the preoperative value of 18.37 ±â€Š1.16 mm (P < .05).We obtained a good fusion rate by using titanium mesh cage spacer with nitinol shape memory alloy loop in patients with atlantoaxial instability. This technique can help surgeons in avoiding vertebral artery injury and reducing bleeding and operation time. Therefore, we suggest that titanium mesh cage spacer combined with nitinol shape memory alloy loop can be a good substitute of autograft for C1-C2 fusion in treating atlantoaxial instabilities.

Minimally Invasive Esophagectomy Provides Equivalent Survival to Open Esophagectomy: An Analysis of the National Cancer Database.

The use of minimally invasive esophagectomy (MIE) is increasing despite limited evidence to support its efficacy. We compared overall survival and perioperative mortality for MIE vs open esophagectomy (OE). We queried the National Cancer Database for all patients having esophagectomy as the primary procedure for primary squamous cell cancer and adenocarcinoma from 2010 through 2012. A propensity score analysis was performed. Postoperative pathology and quality, as well as overall patient survival outcomes, were compared between OE and MIE. The use of MIE increased from 26.9% in 2010 to 36.3% in 2012 (P < 0.001). Of 3032 patients (2050 OE and 982 MIE) who were identified, propensity score matching (1:1) yielded 977 patients in each group. Mean lymph nodes examined were higher in the MIE group (16.3 vs 14.5, P < 0.001). However, final pathologic nodal stage was not significantly different in the matched sample. There was also no difference in pathologic upstaging or margin status between the groups. All other postoperative variables were equivalent, including an average length of stay of 14 days, unplanned readmission rate of 6.5%, and 30-day and 90-day mortality rates of 3% and 7%, respectively. There was no survival difference, with a median survival of 48.7 months for OE and 46.6 months for MIE (Kaplan-Meier analysis, P = 0.376). During the 3-year period analyzed, there were no significant differences in postoperative outcomes and quality metrics between OE and MIE. Although short-term outcomes are limited in the National Cancer Database, MIE appears to have equivalent oncological outcomes and survival when compared with the open approach.

A Study of Deep CNN-Based Classification of Open and Closed Eyes Using a Visible Light Camera Sensor.

The necessity for the classification of open and closed eyes is increasing in various fields, including analysis of eye fatigue in 3D TVs, analysis of the psychological states of test subjects, and eye status tracking-based driver drowsiness detection. Previous studies have used various methods to distinguish between open and closed eyes, such as classifiers based on the features obtained from image binarization, edge operators, or texture analysis. However, when it comes to eye images with different lighting conditions and resolutions, it can be difficult to find an optimal threshold for image binarization or optimal filters for edge and texture extraction. In order to address this issue, we propose a method to classify open and closed eye images with different conditions, acquired by a visible light camera, using a deep residual convolutional neural network. After conducting performance analysis on both self-collected and open databases, we have determined that the classification accuracy of the proposed method is superior to that of existing methods.

Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras.

The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

A Survey on Banknote Recognition Methods by Various Sensors.

Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them.

Increased Prevalence of Ossification of Posterior Longitudinal Ligament and Increased Bone Mineral Density in Patients with Ossification of Nuchal Ligament.

OBJECTIVE: There are also few studies demonstrating the relationship between ossification of nuchal ligament (ONL) and ossification of posterior longitudinal ligament (OPLL). We compared the prevalence, location, and type of OPLL between patients with ONL and matched patients without ONL.We also compared the bone mineral densities (BMDs) between the 2 groups. METHODS: total of 124 cervical ONL patients were enrolled in this study. The control group of 124 patients was matched with 124 patients with ONL by age and sex on a 1:1 basis to minimize confounding factors. We reviewed the prevalence, location, and type of OPLL in both groups. RESULTS: The prevalence of OPLL was almost 2.5 times greater in patients with ONL than those without ONL. The mean value of BMD in patients with ONL was greater at the lumbar spine (L1-L4) than in patients without ONL. The mean T score of the lumbar spine was 0.25±1.68 in the patients with ONL and -0.73±1.64 in the patients without ONL. CONCLUSION: The prevalence of OPLL in patients with ONL was significantly higher than in patients without ONL. Because ONL is innocuous and may be seen more readily than OPLL on simple cervical radiographs, clinicians should consider the possibility of coexisting OPLL when ONL, especially extensive ONL, is detected in patients with neck pain, radiculopathy, or myelopathy, to facilitate proper treatment.

The effects of radicular dentine treated with double antibiotic paste and ethylenediaminetetraacetic acid on the attachment and proliferation of dental pulp stem cells.

AIM: This study explored the effects of dentine treated with two concentrations of double antibiotic paste (DAP) and ethylenediaminetetraacetic acid (EDTA) on the attachment and proliferation of dental pulp stem cells (DPSCs). MATERIALS AND METHODS: Radicular dentine samples were prepared with identical dimensions and randomized into six groups (n = 4). Four groups were treated with double antibiotic paste (DAP) at concentrations of 500 mg ml(-1) or 1 mg ml(-1) with or without EDTA. The other two groups were treated with EDTA only or received no treatment. DPSCs were seeded on each dentine sample (10 000 cells per sample). Lactate dehydrogenase activity assays were used to calculate the attached DPSCs after 1 day of incubation. Water soluble tetrazolium assays were performed to investigate DPSCs proliferation on the treated dentine samples after three additional days of incubation. Two-way anova followed by Tukey-Kramer tests was used for statistical analyses (α = 0.05). RESULTS: Dentine treated with 1 or 500 mg ml(-1) of DAP followed by EDTA caused significant increases in DPSCs attachment compared to the dentine treated with the DAP alone. The 500 mg ml(-1) of DAP with or without EDTA caused significant reductions in DPSCs proliferation. However, the treatment of dentine with 1 mg ml(-1) of DAP did not have significant negative effects on DPSCs proliferation regardless of the use of EDTA. CONCLUSION: The use of 1 mg ml(-1) of DAP followed by 10 min of irrigation with EDTA in endodontic regeneration procedure may have no negative effects on the attachment and proliferation of DPSCs.

Cerebral infarction presenting with unilateral isolated foot drop.

Weakness of the dorsiflexor muscles of the ankle or toe, referred to as foot drop, is a relatively common presentation. In most cases, foot drop is caused by a lower motor neuron disease such as peroneal peripheral neuropathy, L4-5 radiculopathic sciatic neuropathy, or polyneuropathy. Although upper motor neuron lesions can present as foot drop, the incidence is very rare. Here, we report an extremely rare case in which foot drop was the only presenting symptom of cerebral infarction.

Hot carrier-selective chemical reactions on Ag(110).

Here, we show that the pathways, products, and efficiencies of reactions occurring on a metal surface can be spatially modulated by varying the type and energy of hot carriers produced by injecting tunneling electrons or holes from a scanning tunneling microscope tip into the metal surface. Control over the metal surface reactions was demonstrated for the large-scale dissociation reaction of O2 molecules on a Ag(110) surface. Hot electrons (or holes) transported through the metal surface to chemisorbed O2 selectively dissociated the molecule into two oxygen atoms separated along the [110] (or [001]) lattice direction. The reaction selectivity was enhanced compared to the selectivity of a direct reaction involving tunneling carriers.