A deep learning framework for quality assessment and restoration in video endoscopy

Endoscopy is a routine imaging technique used for both diagnosis and minimally invasive surgical treatment. Artifacts such as motion blur, bubbles, specular reflections, floating objects and pixel saturation impede the visual interpretation and the automated analysis of endoscopy videos. Given the widespread use of endoscopy in different clinical applications, we contend that the robust and reliable identification of such artifacts and the automated restoration of corrupted video frames is a fundamental medical imaging problem. Existing state-of-the-art methods only deal with the detection and restoration of selected artifacts. However, typically endoscopy videos contain numerous artifacts which motivates to establish a comprehensive solution. We propose a fully automatic framework that can: 1) detect and classify six different primary artifacts, 2) provide a quality score for each frame and 3) restore mildly corrupted frames. To detect different artifacts our framework exploits fast multi-scale, single stage convolutional neural network detector. We introduce a quality metric to assess frame quality and predict image restoration success. Generative adversarial networks with carefully chosen regularization are finally used to restore corrupted frames. Our detector yields the highest mean average precision (mAP at 5% threshold) of 49.0 and the lowest computational time of 88 ms allowing for accurate real-time processing. Our restoration models for blind deblurring, saturation correction and inpainting demonstrate significant improvements over previous methods. On a set of 10 test videos we show that our approach preserves an average of 68.7% which is 25% more frames than that retained from the raw videos.Comment: 14 page

Quality Assessment of Mycobacterium tuberculosis Genotyping in a Large Laboratory Network

Quality assessment exercises were conducted to evaluate the reproducibility of IS6110 DNA fingerprinting performed by eight laboratories in the National Tuberculosis Genotyping and Surveillance Network. Three panels, each with 8 to 16 isolates, were typed at all laboratories, resulting in 280 images. When the pattern obtained by the majority for each isolate was used as the standard, exact matches were obtained for 73% of patterns; 90% and 97% of patterns matched within one- and two-band differences, respectively. A second approach involved retyping of randomly selected isolates at the Centers for Disease Control and Prevention. Retyping was done for 8–19 isolates per laboratory (76 total). Paired images matched exactly for 54% of isolates and within one and two band differences, 78% and 93%, respectively. We evaluated reasons for mismatching. We also evaluated the reproducibility of spoligotyping using a test panel of 13 isolates; a discrepancy of 1 in 91 results was noted

National Tuberculosis Genotyping and Surveillance Network: Design and Methods

The National Tuberculosis Genotyping and Surveillance Network was established in 1996 to perform a 5-year, prospective study of the usefulness of genotyping Mycobacterium tuberculosis isolates to tuberculosis control programs. Seven sentinel sites identified all new cases of tuberculosis, collected information on patients and contacts, and obtained patient isolates. Seven genotyping laboratories performed DNA fingerprinting analysis by the international standard IS6110 method. BioImage Whole Band Analyzer software was used to analyze patterns, and distinct patterns were assigned unique designations. Isolates with six or fewer bands on IS6110 patterns were also spoligotyped. Patient data and genotyping designations were entered in a relational database and merged with selected variables from the national surveillance database. In two related databases, we compiled the results of routine contact investigations and the results of investigations of the relationships of patients who had isolates with matching genotypes. We describe the methods used in the study

Towards real-time detection of squamous pre-cancers from oesophageal endoscopic videos

This study investigates the feasibility of applying state of the art deep learning techniques to detect precancerous stages of squamous cell carcinoma (SCC) cancer in real time to address the challenges while diagnosing SCC with subtle appearance changes as well as video processing speed. Two deep learning models are implemented, which are to determine artefact of video frames and to detect, segment and classify those no-artefact frames respectively. For detection of SCC, both mask-RCNN and YOLOv3 architectures are implemented. In addition, in order to ascertain one bounding box being detected for one region of interest instead of multiple duplicated boxes, a faster non-maxima suppression technique (NMS) is applied on top of predictions. As a result, this developed system can process videos at 16-20 frames per second. Three classes are classified, which are ‘suspicious’, ‘high grade’ and ‘cancer’ of SCC. With the resolution of 1920x1080 pixels of videos, the average processing time while apply YOLOv3 is in the range of 0.064-0.101 seconds per frame, i.e. 10-15 frames per second, while running under Windows 10 operating system with 1 GPU (GeForce GTX 1060). The averaged accuracies for classification and detection are 85% and 74% respectively. Since YOLOv3 only provides bounding boxes, to delineate lesioned regions, mask-RCNN is also evaluated. While better detection result is achieved with 77% accuracy, the classification accuracy is similar to that by YOLOYv3 with 84%. However, the processing speed is more than 10 times slower with an average of 1.2 second per frame due to creation of masks. The accuracy of segmentation by mask-RCNN is 63%. These results are based on the date sets of 350 images. Further improvement is hence in need in the future by collecting, annotating or augmenting more datasets

Molecular Epidemiology of Tuberculosis in a Sentinel Surveillance Population

We conducted a population-based study to assess demographic and risk-factor correlates for the most frequently occurring Mycobacterium tuberculosis genotypes from tuberculosis (TB) patients. The study included all incident, culture-positive TB patients from seven sentinel surveillance sites in the United States from 1996 to 2000. M. tuberculosis isolates were genotyped by IS6110-based restriction fragment length polymorphism and spoligotyping. Genotyping was available for 90% of 11,923 TB patients. Overall, 48% of cases had isolates that matched those from another patient, including 64% of U.S.-born and 35% of foreign-born patients. By logistic regression analysis, risk factors for clustering of genotypes were being male, U.S.-born, black, homeless, and infected with HIV; having pulmonary disease with cavitations on chest radiograph and a sputum smear with acid-fast bacilli; and excessive drug or alcohol use. Molecular characterization of TB isolates permitted risk correlates for clusters and specific genotypes to be described and provided information regarding cluster dynamics over time

Transfer learning for endoscopy disease detection and segmentation with mask-RCNN benchmark architecture

We proposed and implemented a disease detection and semantic segmentation pipeline using a modified mask-RCNN infrastructure model on the EDD2020 dataset1. On the images provided for the phase-I test dataset, for ’BE’, we achieved an average precision of 51.14%, for ’HGD’ and ’polyp’ it is 50%. However, the detection score for ’suspicious’ and ’cancer’ were low. For phase-I, we achieved a dice coefficient of 0.4562 and an F2 score of 0.4508. We noticed the missed and mis-classification was due to the imbalance between classes. Hence, we applied a selective and balanced augmentation stage in our architecture to provide more accurate detection and segmentation. We observed an increase in detection score to 0.29 on phase-II images after balancing the dataset from our phase-I detection score of 0.24. We achieved an improved semantic segmentation score of 0.62 from our phase-I score of 0.52

Gastrointestinal Ultrasound in Functional Disorders of the Gastrointestinal Tract - EFSUMB Consensus Statement

Abdominal ultrasonography and intestinal ultrasonography are widely used as first diagnostic tools for investigating patients with abdominal symptoms, mainly for excluding organic diseases. However, gastrointestinal ultrasound (GIUS), as a real-time diagnostic imaging method, can also provide information on motility, flow, perfusion, peristalsis, and organ filling and emptying, with high temporal and spatial resolution. Thanks to its noninvasiveness and high repeatability, GIUS can investigate functional gastrointestinal processes and functional gastrointestinal diseases (FGID) by studying their behavior over time and their response to therapy and providing insight into their pathophysiologic mechanisms. The European Federation of Societies for Ultrasound in Medicine and Biology (EFSUMB) has established a Task Force Group consisting of GIUS experts, which developed clinical recommendations and guidelines on the role of GIUS in several acute and chronic gastrointestinal diseases. This review is dedicated to the role of GIUS in assisting the diagnosis of FGID and particularly in investigating patients with symptoms of functional disorders, such as dysphagia, reflux disorders, dyspepsia, abdominal pain, bloating, and altered bowel habits. The available scientific evidence of GIUS in detecting, assessing, and investigating FGID are reported here, while highlighting sonographic findings and its usefulness in a clinical setting, defining the actual and potential role of GIUS in the management of patients, and providing information regarding future applications and research.publishedVersio