scholarly journals Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy

2020 ◽  
Author(s):  
Debesh Jha ◽  
Sharib Ali ◽  
Krister Emanuelsen ◽  
Steven Hicks ◽  
Vajira Thambawita ◽  
...  
Keyword(s):  
Gastrointestinal Endoscopy ◽  
Ground Truth ◽  
Jaccard Index ◽  
Dice Coefficient ◽  
Therapeutic Tool ◽  
Current Standard ◽  
Qualitative And Quantitative ◽  
Quantitative Results ◽  
Bounding Boxes

Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually, the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost.This can lead to poor follow-up and bothersome reassessment difficulties post-treatment. To improve the current standard and also to foster more research on the topic we have released the "Kvasir-Instrument" dataset which consists of 590 annotated frames containing GI procedure tools such as snares, balloons and biopsy forceps, etc. Beside of the images, the dataset includes ground truth masks and bounding boxes and has been verified by two expert GI endoscopists. Additionally, we provide a baseline for the segmentation of the GI tools to promote research and algorithm development. We obtained a dice coefficient score of 0.9158 and a Jaccard index of 0.8578 using a classical U-Net architecture. A similar dice coefficient score was observed for DoubleUNet. The qualitative results showed that the model did not work for the images with specularity and the frames with multiple instruments, while the best result for both methods was observed on all other types of images. Both, qualitative and quantitative results show that the model performs reasonably good, but there is a large potential for further improvements. Benchmarking using the dataset provides an opportunity for researchers to contribute to the field of automatic endoscopic diagnostic and therapeutic tool segmentation for GI endoscopy.

scholarly journals Accurate Instance Segmentation in Pediatric Elbow Radiographs

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7966
Author(s):  
Dixiao Wei ◽  
Qiongshui Wu ◽  
Xianpei Wang ◽  
Meng Tian ◽  
Bowen Li
Keyword(s):  
Elbow Joint ◽  
Segmentation Method ◽  
Qualitative And Quantitative ◽  
Robust Segmentation ◽  
Local Contexts ◽  
Good Potential ◽  
Quantitative Results ◽  
Bounding Boxes ◽  
Global And Local ◽  
Instance Segmentation

Radiography is an essential basis for the diagnosis of fractures. For the pediatric elbow joint diagnosis, the doctor needs to diagnose abnormalities based on the location and shape of each bone, which is a great challenge for AI algorithms when interpreting radiographs. Bone instance segmentation is an effective upstream task for automatic radiograph interpretation. Pediatric elbow bone instance segmentation is a process by which each bone is extracted separately from radiography. However, the arbitrary directions and the overlapping of bones pose issues for bone instance segmentation. In this paper, we design a detection-segmentation pipeline to tackle these problems by using rotational bounding boxes to detect bones and proposing a robust segmentation method. The proposed pipeline mainly contains three parts: (i) We use Faster R-CNN-style architecture to detect and locate bones. (ii) We adopt the Oriented Bounding Box (OBB) to improve the localizing accuracy. (iii) We design the Global-Local Fusion Segmentation Network to combine the global and local contexts of the overlapped bones. To verify the effectiveness of our proposal, we conduct experiments on our self-constructed dataset that contains 1274 well-annotated pediatric elbow radiographs. The qualitative and quantitative results indicate that the network significantly improves the performance of bone extraction. Our methodology has good potential for applying deep learning in the radiography’s bone instance segmentation.

scholarly journals Deep Sedation for Pediatric Parotid Sialendoscopy in Juvenile Recurrent Parotitis

2021 ◽  
Vol 10 (2) ◽  
pp. 276
Author(s):  
Pasquale Capaccio ◽  
Andrea Palermo ◽  
Paolo Lucchinelli ◽  
Tiziana Marchesi ◽  
Sara Torretta ◽  
...  
Keyword(s):  
Adverse Effects ◽  
Minimally Invasive ◽  
General Anesthesia ◽  
Gastrointestinal Endoscopy ◽  
Deep Sedation ◽  
Intravenous Bolus ◽  
Therapeutic Tool ◽  
Juvenile Recurrent Parotitis ◽  
Parotid Swelling

Sialendoscopy is a minimally invasive diagnostic and therapeutic tool for juvenile recurrent parotitis (JRP); the procedure is under general anesthesia, but local anesthesia has been used for sialendoscopy in children >8 years. Based on the experience in children with sedation for gastrointestinal endoscopy, we investigated the reliability and safety of deep sedation for sialendoscopy in JRP. Six children (3 females, 6–13 years) with episodes of parotid swelling underwent interventional (duct dilation and steroid irrigation) sialendoscopy with intravenous bolus of 1 mg/kg propofol and 1 mcg/kg fentanyl, and continuous infusion of 2 mg/kg/h propofol. Sialendoscopy under deep sedation was successfully performed in all the patients; the procedure was well tolerated, without any adverse effects. One event of full awakening was registered and promptly solved without needing to interrupt the procedure. Effectiveness of sialendoscopy under deep sedation was subjectively attested by high positive scores obtained at post-operative standardized questionnaires administered to the patients and their parents, and objectively by the lack of clinical recurrences during the follow-up. The combination of propofol and fentanyl seems to be a reliable and safe means of sedating children with JRP undergoing sialendoscopy.

scholarly journals BREAST CANCER SEGMENTATION OF MAMMOGRAPHICS IMAGES USING GENERATIVE

2021 ◽  
Vol 57 (2) ◽  
pp. 247-255
Author(s):  
Swathi N ◽  
◽  
T. Christy Bobby ◽  
Keyword(s):  
Breast Cancer ◽  
Ground Truth ◽  
Pectoral Muscle ◽  
Jaccard Index ◽  
Neural Nets ◽  
Neural Net ◽  
Dice Coefficient ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cancer Tumor

Segmentation of breast cancer tumor plays an important role in identifying the location of the tumor, to know the shape of tumor and hence the stage of breast cancer. This paper deals with the segmentation of tumor from whole mammographic mass images using Generative Adversarial Network (GAN). A mini dataset was considered with mammograms and their corresponding ground truth images. Pre-processing like image format conversion, enhancement, pectoral muscle removal and resizing was performed on raw mammogram images. GANs have two neural nets called generative and discriminative networks that compete against each other to obtain the segmentation output. PIX2PIX is a conditional GAN variant which has U-Net as the Generator network and a simple deep neural net as the discriminator. The input to the network was pair of pre-processed mass image and the associated ground truth. A binary image with highlighted tumor was obtained as output. The performance of GAN was evaluated by plotting Generator and discriminator loss. The segmented output was compared with corresponding ground truth. Metrics like Jaccard index, Jaccard distance and Dice-coefficient were calculated. A Dice-coefficient and Jaccard index of 90% and 88.38% was achieved. In future, higher accuracy could be achieved by involving larger dataset to make the system robust.

Brain FDG PET for the Etiological Diagnosis of Clinically Uncertain Cognitive Impairment During Delirium in Remission

2020 ◽  
Vol 77 (4) ◽  
pp. 1609-1622
Author(s):  
Franziska Mathies ◽  
Catharina Lange ◽  
Anja Mäurer ◽  
Ivayla Apostolova ◽  
Susanne Klutmann ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Fdg Pet ◽  
False Negative ◽  
Ground Truth ◽  
Etiological Diagnosis ◽  
Geriatric Unit ◽  
Positron Emission ◽  
The Brain ◽  
Hospitalized Geriatric Patients

Background: Positron emission tomography (PET) of the brain with 2-[F-18]-fluoro-2-deoxy-D-glucose (FDG) is widely used for the etiological diagnosis of clinically uncertain cognitive impairment (CUCI). Acute full-blown delirium can cause reversible alterations of FDG uptake that mimic neurodegenerative disease. Objective: This study tested whether delirium in remission affects the performance of FDG PET for differentiation between neurodegenerative and non-neurodegenerative etiology of CUCI. Methods: The study included 88 patients (82.0±5.7 y) with newly detected CUCI during hospitalization in a geriatric unit. Twenty-seven (31%) of the patients were diagnosed with delirium during their current hospital stay, which, however, at time of enrollment was in remission so that delirium was not considered the primary cause of the CUCI. Cases were categorized as neurodegenerative or non-neurodegenerative etiology based on visual inspection of FDG PET. The diagnosis at clinical follow-up after ≥12 months served as ground truth to evaluate the diagnostic performance of FDG PET. Results: FDG PET was categorized as neurodegenerative in 51 (58%) of the patients. Follow-up after 16±3 months was obtained in 68 (77%) of the patients. The clinical follow-up diagnosis confirmed the FDG PET-based categorization in 60 patients (88%, 4 false negative and 4 false positive cases with respect to detection of neurodegeneration). The fraction of correct PET-based categorization did not differ between patients with delirium in remission and patients without delirium (86% versus 89%, p = 0.666). Conclusion: Brain FDG PET is useful for the etiological diagnosis of CUCI in hospitalized geriatric patients, as well as in patients with delirium in remission.

Evaluation of medical care for Diabetic and Hypertensive patients in primary care in Mexico: Observational retrospective study (Preprint)

2020 ◽  
Author(s):  
Agustin Lara-Esqueda ◽  
Sergio A Zaizar-Fregoso ◽  
Violeta M Madrigal-Perez ◽  
Mario Ramirez-Flores ◽  
Daniel A Montes-Galindo ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Medical Care ◽  
Serum Lipids ◽  
Quality Care ◽  
Premature Death ◽  
Current Standard ◽  
Anonymized Data ◽  
Quality Of Medical Care

BACKGROUND Diabetes Mellitus is a worldwide health problem and the leading cause of premature death with increasing prevalence over time. Usually, along with it, Hypertension presents and acts as another risk factor that increases mortality risk. Both diseases impact the country's health while also producing an economic burden for society, causing billions of dollars to be invested in their management. OBJECTIVE The present study evaluated the quality of medical care for patients diagnosed with diabetes mellitus (DM), hypertension (HBP), and both pathologies (DM+HBP) within a public health system in Mexico, according to the official Mexican standard for each pathology. METHODS 45,498 patients were included from 2012 to 2015. All information was taken from the electronic medical records database, exported as anonymized data for research purposes. Each patient record was compared against the standard to test the quality of medical care. RESULTS Glycemia with hypertension goals reached 29.6% in DM+HBP, 48.6% in DM, and 53.2% in HBP. The goals of serum lipids were reached by 3% in DM+HBP, 5% in DM, and 0.2% in HBP. Glycemia, hypertension, and LDL cholesterol reached 0.04%. 15% of patients had an undiagnosed disease of diabetes or hypertension. Clinical follow-up examinations reached 20% for foot examination and clinical eye examination in the whole population. Specialty referral reached 1% in angiology or cardiology in the whole population. CONCLUSIONS Goals for glycemic and hypertension reached 50% in the overall population, while serum lipids, clinical follow-up examinations, and referral to a specialist were deficient. Patients who had both diseases had more consultations, better control for hypertension and lipids, but inferior glycemic control. Overall, quality care for DM and/or HBP has not been met according to the standards. While patients with DM and HBP do not have a current standard to evaluate their own needs.

scholarly journals RobotP: A Benchmark Dataset for 6D Object Pose Estimation

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1299
Author(s):  
Honglin Yuan ◽  
Tim Hoogenkamp ◽  
Remco C. Veltkamp
Keyword(s):  
Pose Estimation ◽  
Ground Truth ◽  
3D Models ◽  
Depth Image ◽  
Great Success ◽  
Estimation Algorithms ◽  
Depth Images ◽  
Object Pose Estimation ◽  
Image Pairs ◽  
Bounding Boxes

Deep learning has achieved great success on robotic vision tasks. However, when compared with other vision-based tasks, it is difficult to collect a representative and sufficiently large training set for six-dimensional (6D) object pose estimation, due to the inherent difficulty of data collection. In this paper, we propose the RobotP dataset consisting of commonly used objects for benchmarking in 6D object pose estimation. To create the dataset, we apply a 3D reconstruction pipeline to produce high-quality depth images, ground truth poses, and 3D models for well-selected objects. Subsequently, based on the generated data, we produce object segmentation masks and two-dimensional (2D) bounding boxes automatically. To further enrich the data, we synthesize a large number of photo-realistic color-and-depth image pairs with ground truth 6D poses. Our dataset is freely distributed to research groups by the Shape Retrieval Challenge benchmark on 6D pose estimation. Based on our benchmark, different learning-based approaches are trained and tested by the unified dataset. The evaluation results indicate that there is considerable room for improvement in 6D object pose estimation, particularly for objects with dark colors, and photo-realistic images are helpful in increasing the performance of pose estimation algorithms.

scholarly journals Fully-automated root image analysis (faRIA)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Narendra Narisetti ◽  
Michael Henke ◽  
Christiane Seiler ◽  
Astrid Junker ◽  
Jörn Ostermann ◽  
...  
Keyword(s):  
Near Infrared ◽  
Climatic Factors ◽  
Plant Root ◽  
Ground Truth ◽  
Parameter Tuning ◽  
Dice Coefficient ◽  
Powerful Analytical Tool ◽  
Manual Interaction ◽  
And Function ◽  
Noisy Background

AbstractHigh-throughput root phenotyping in the soil became an indispensable quantitative tool for the assessment of effects of climatic factors and molecular perturbation on plant root morphology, development and function. To efficiently analyse a large amount of structurally complex soil-root images advanced methods for automated image segmentation are required. Due to often unavoidable overlap between the intensity of fore- and background regions simple thresholding methods are, generally, not suitable for the segmentation of root regions. Higher-level cognitive models such as convolutional neural networks (CNN) provide capabilities for segmenting roots from heterogeneous and noisy background structures, however, they require a representative set of manually segmented (ground truth) images. Here, we present a GUI-based tool for fully automated quantitative analysis of root images using a pre-trained CNN model, which relies on an extension of the U-Net architecture. The developed CNN framework was designed to efficiently segment root structures of different size, shape and optical contrast using low budget hardware systems. The CNN model was trained on a set of 6465 masks derived from 182 manually segmented near-infrared (NIR) maize root images. Our experimental results show that the proposed approach achieves a Dice coefficient of 0.87 and outperforms existing tools (e.g., SegRoot) with Dice coefficient of 0.67 by application not only to NIR but also to other imaging modalities and plant species such as barley and arabidopsis soil-root images from LED-rhizotron and UV imaging systems, respectively. In summary, the developed software framework enables users to efficiently analyse soil-root images in an automated manner (i.e. without manual interaction with data and/or parameter tuning) providing quantitative plant scientists with a powerful analytical tool.

scholarly journals Affective communication: a mixed method investigation into COVID-19 outbreak communication using the Taiwanese government Facebook page

2021 ◽  
pp. 175797592110035
Author(s):  
Chia Yu Lien ◽  
Yun-Hsuan Wu
Keyword(s):  
Social Media ◽  
Mixed Method ◽  
Health Authority ◽  
Disease Outbreak ◽  
Government Agencies ◽  
Affective Communication ◽  
Frontline Workers ◽  
The Public ◽  
Qualitative And Quantitative ◽  
Quantitative Results

The COVID-19 outbreak has created an unprecedented challenge for governments to convey information to the public, and social media has become a critical method of COVID-19 communication in Taiwan. Objectives: This study examines a total of 1128 Facebook posts published by Taiwan’s principal health authority from December 1, 2019 to May 31, 2020. Methods: Using both qualitative and quantitative approaches, this study investigates strategies used by the Taiwan government to communicate the COVID-19 outbreak and public responses toward these strategies. Result: Novel uses of Facebook posts on outbreak communication were identified, including solidarity, reviews of actions, press conferences, and the use of animal and cartoon images. Quantitative results showed that the public responded significantly more frequently to messages generating positive affects, such as posts that reviewed government actions and public efforts; posts that expressed thanks, approval, or comradeship; and posts that paired text with photographs of frontline workers or cute animals. Conclusion: These results suggest that, amid a disease outbreak, the public not only look for updated situations and guidelines but also for affective affirmation from government agencies.

Multidirection Validation of a Finite Element 50th Percentile Male Hybrid III Anthropomorphic Test Device for Spaceflight Applications

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Derek A. Jones ◽  
James P. Gaewsky ◽  
Mona Saffarzadeh ◽  
Jacob B. Putnam ◽  
Ashley A. Weaver ◽  
...  
Keyword(s):  
Finite Element ◽  
Injury Risk ◽  
Fe Model ◽  
Fe Modeling ◽  
Test Device ◽  
Qualitative And Quantitative ◽  
Hybrid Iii ◽  
Physical Tests ◽  
Quantitative Results ◽  
Male Hybrid

The use of anthropomorphic test devices (ATDs) for calculating injury risk of occupants in spaceflight scenarios is crucial for ensuring the safety of crewmembers. Finite element (FE) modeling of ATDs reduces cost and time in the design process. The objective of this study was to validate a Hybrid III ATD FE model using a multidirection test matrix for future spaceflight configurations. Twenty-five Hybrid III physical tests were simulated using a 50th percentile male Hybrid III FE model. The sled acceleration pulses were approximately half-sine shaped, and can be described as a combination of peak acceleration and time to reach peak (rise time). The range of peak accelerations was 10–20 G, and the rise times were 30–110 ms. Test directions were frontal (−GX), rear (GX), vertical (GZ), and lateral (GY). Simulation responses were compared to physical tests using the correlation and analysis (CORA) method. Correlations were very good to excellent and the order of best average response by direction was −GX (0.916±0.054), GZ (0.841±0.117), GX (0.792±0.145), and finally GY (0.775±0.078). Qualitative and quantitative results demonstrated the model replicated the physical ATD well and can be used for future spaceflight configuration modeling and simulation.

Nonlinear Analysis of a Rigid Rotor on Magnetic Bearings

1995 ◽  
Author(s):  
C. Nataraj
Keyword(s):  
Nonlinear Analysis ◽  
Equations Of Motion ◽  
Forced Response ◽  
Magnetic Bearings ◽  
Rigid Rotor ◽  
Stability Criteria ◽  
Safe Operation ◽  
Qualitative And Quantitative ◽  
Quantitative Results ◽  
Nonlinear Equations Of Motion

A simple model of a rigid rotor supported on magnetic bearings is considered. A proportional control architecture is assumed, the nonlinear equations of motion are derived and some essential nondimensional parameters are identified. The free and forced response of the system is analyzed using techniques of nonlinear analysis. Both qualitative and quantitative results are obtained and stability criteria are derived for safe operation of the system.

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