Computers in Biology and Medicine / Survey on computer aided decision support for diagnosis of celiac disease

Celiac disease (CD) is a complex autoimmune disorder in genetically predisposed individuals of all age groups triggered by the ingestion of food containing gluten. A reliable diagnosis is of high interest in view of embarking on a strict gluten-free diet, which is the CD treatment modality of first choice. The gold standard for diagnosis of CD is currently based on a histological confirmation of serology, using biopsies performed during upper endoscopy. Computer aided decision support is an emerging option in medicine and endoscopy in particular. Such systems could potentially save costs and manpower while simultaneously increasing the safety of the procedure. Research focused on computer-assisted systems in the context of automated diagnosis of CD has started in 2008. Since then, over 40 publications on the topic have appeared. In this context, data from classical flexible endoscopy as well as wireless capsule endoscopy (WCE) and confocal laser endomicrosopy (CLE) has been used. In this survey paper, we try to give a comprehensive overview of the research focused on computer-assisted diagnosis of CD.FWF 24366(VLID)223161

TOWARDS HIGH RESOLUTION ENDOSCOPIC OPTICAL COHERENCE TOMOGRAPHY FOR IMAGING INTERNAL ORGANS

Optical coherence tomography (OCT) is a light based interferometric imaging technique that can provide high resolution (5-20 µm at 1300 nm), depth resolved, images in real-time. With recent advances in portable low coherent light sources for OCT it is now possible to achieve ultrahigh axial resolutions (≤ 3 µm) by moving to shorter central wavelengths such as 800 nm while utilizing a broad spectral bandwidth. Our goal was to push ultrahigh resolution OCT technology to in vivo imaging of internal organs for endoscopic assessment of tissue microstructure. This dissertation is separated into technological developments and biomedical imaging studies. Technological developments in this dissertation included development of a high speed, ultrahigh resolution distal scanning catheter. This catheter was based upon a miniature DC micromotor capable of rotational velocities in excess of 100 rps, a diffractive compound lens design that minimized chromatic aberrations, and a mechanical assembly that limited field of view blockage to less than 7.5% and maintained an outer diameter of 1.78 mm (with plastic sheath). In conjunction with the algorithm described in chapter 4 to correct for non-uniform rotational distortion, the overall imaging system was capable of high quality endoscopic imaging of internal organs in vivo. Equipped with the ultrahigh resolution endoscopic OCT system, imaging was performed in small airways and colorectal cancer. Imaging results demonstrated the ability to directly visualization of microstructural details such as airway smooth muscle in the small airways representing a major step forward in pulmonary imaging. With the ability to visualize airway smooth muscle, morphological changes in COPD and related diseases can be further investigated. Additionally, longitudinal changes in an ETBF induced colon cancer model in APCMin mice were studied as well. Quantitative assessment of tissue microarchitecture was performed by measuring the attenuation coefficient to find a bimodal distribution separating normal healthy tissue from polyps. Finally, results from two additional projects were also demonstrated. Chapter 7 shows some results from vascular imaging in a tumor angiogenesis model and middle cerebral artery occlusion model. Chapter 8 describes an endoscopic multimodal OCT and fluorescence imaging platform with results in ex vivo rabbit esophagus

EndoSLAM Dataset and An Unsupervised Monocular Visual Odometry and Depth Estimation Approach for Endoscopic Videos: Endo-SfMLearner

Deep learning techniques hold promise to develop dense topography reconstruction and pose estimation methods for endoscopic videos. However, currently available datasets do not support effective quantitative benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM dataset consisting of 3D point cloud data for six porcine organs, capsule and standard endoscopy recordings as well as synthetically generated data. A Panda robotic arm, two commercially available capsule endoscopes, two conventional endoscopes with different camera properties, and two high precision 3D scanners were employed to collect data from 8 ex-vivo porcine gastrointestinal (GI)-tract organs. In total, 35 sub-datasets are provided with 6D pose ground truth for the ex-vivo part: 18 sub-dataset for colon, 12 sub-datasets for stomach and 5 sub-datasets for small intestine, while four of these contain polyp-mimicking elevations carried out by an expert gastroenterologist. Synthetic capsule endoscopy frames from GI-tract with both depth and pose annotations are included to facilitate the study of simulation-to-real transfer learning algorithms. Additionally, we propound Endo-SfMLearner, an unsupervised monocular depth and pose estimation method that combines residual networks with spatial attention module in order to dictate the network to focus on distinguishable and highly textured tissue regions. The proposed approach makes use of a brightness-aware photometric loss to improve the robustness under fast frame-to-frame illumination changes. To exemplify the use-case of the EndoSLAM dataset, the performance of Endo-SfMLearner is extensively compared with the state-of-the-art. The codes and the link for the dataset are publicly available at https://github.com/CapsuleEndoscope/EndoSLAM. A video demonstrating the experimental setup and procedure is accessible through https://www.youtube.com/watch?v=G_LCe0aWWdQ.Comment: 27 pages, 16 figure

World Journal of Gastroenterology / Computer-aided texture analysis combined with experts' knowledge : improving endoscopic celiac disease diagnosis

AIM: To further improve the endoscopic detection of intestinal mucosa alterations due to celiac disease (CD). METHODS: We assessed a hybrid approach based on the integration of expert knowledge into the computer-based classification pipeline. A total of 2835 endoscopic images from the duodenum were recorded in 290 children using the modified immersion technique (MIT). These children underwent routine upper endoscopy for suspected CD or non-celiac upper abdominal symptoms between August 2008 and December 2014. Blinded to the clinical data and biopsy results, three medical experts> visually classified each image as normal mucosa (Marsh-0) or villous atrophy (Marsh-3). The experts decisions were further integrated into state-of-the-art texture recognition systems. Using the biopsy results as the reference standard, the classification accuracies of this hybrid approach were compared to the experts diagnoses in 27 different settings. RESULTS: Compared to the experts diagnoses, in 24 of 27 classification settings (consisting of three imaging modalities, three endoscopists and three classification approaches), the best overall classification accuracies were obtained with the new hybrid approach. In 17 of 24 classification settings, the improvements achieved with the hybrid approach were statistically significant (P < 0.05). Using the hybrid approach classification accuracies between 94% and 100% were obtained. Whereas the improvements are only moderate in the case of the most experienced expert, the results of the less experienced expert could be improved significantly in 17 out of 18 classification settings. Furthermore, the lowest classification accuracy, based on the combination of one database and one specific expert, could be improved from 80% to 95% (P < 0.001). CONCLUSION: The overall classification performance of medical experts, especially less experienced experts, can be boosted significantly by integrating expert knowledge into computer-aided diagnosis systems.KLI 429-B13(VLID)215382

Development of Optical Devices for Digital Medicine

Department of Biomedical EngineeringAdvances of technology have made a revolution that interconnects industrial devices and fuses the boundaries of digital, physical and biological spaces. These technologies such as cloud computing, 3D printing technology, big data, internet of things (IOT), artificial intelligence (AI), and maturity of system integrations have been improved every year, changing our daily life quickly in intelligent and convenient ways. In this days, these explosions of technology, changing the way we live and think, is referred to 4th industrial revolution. As we know, every industry is affected by the new waves of technologies, digitalization and connectivity, and the biomedical or medical field is no exception. Healthcare fields have benefited mostly from recent technical improvements, revolutionizing the medical systems in many terms in cost-effective ways. Particularly, ???digital medicine??? has been recently came into the limelight as one of the uprising fields. In digital medicine, traditional medical devices and diagnostic programs have become miniaturized, digitalized, and automated. As taking advantages of digital medicine, specific fields related to digital pathology, point-of-care (POC) diagnostics, and application of deep learning or machine learning technologies have shown the great potentials not only in biomedical academia but also in the revenues of their markets. It allows to connect devices, hospital equipment, and to accelerate efficiencies in health service such as diagnosis, and to reduce the cost of services. Moreover, interconnection between advanced technologies has been improved the access of healthcare to the places where hospital or medical services are limited. Furthermore, artificial intelligence has shown promising results related to disease screening especially using medical images. Although fields in digital medicine are prospering, still there are limitations that needs to be overcome in order to provide further advanced health services to patients in the various situations. In digital pathology, improvements of microscopic technologies, internets, and storage capabilities have reduced the time-consuming processes. The simple transformation of microscopic image to digital have successfully alternated many limitations in the analogue histopathology workflow to efficient and cost saving ways. However, tissue staining is currently referred as one of the bottleneck that makes workflow still lengthy, labor-intensive, and costly. In the POC diagnostic fields, various digitalized portable smartphone-based diagnostic devices have been introduced as alternatives to conventional medical services. These devices have provided the quality assurance of diagnostics by taking advantages of sharing, and quantitative analysis of digital information. However, most of these works have been focused on replacing diagnostic process which mostly done in laboratory settings. As medical imaging devices and trained clinicians or practitioners are limited, there are also high demands on clinical imaging-based diagnostics in developing countries. In this thesis, computational microscope using patterned NIR illumination was developed for label-free quantitative differential phase tissue imaging to bypass the staining process of the pathology workflow. This system overcame the limitations found in the conventional quantitative differential phase contrast in a LED array microscope, allowing to captured light scattering and absorbing specimen while maintaining weak object approximation. Moreover, portable endoscope system was developed integrating the additive production technologies (3D printing), ICT, and optics for POC diagnostics. This innovative POC endoscope demonstrated comparable imaging capability to that of commercialized clinical endoscope system. Furthermore, deep learning and machine learning models have been trained and applied to each devices, respectively. Generative adversarial network (GAN) was applied to our NIR-based QPI system to virtually stain the label-free QPI which look comparable to image that is captured from bright field microscope using labeled tissue. Lastly, POC automated cervical cancer screening system was developed utilizing smartphone-based endoscope system as well as training the machine learning algorithm. 3-5% of acetic acid was applied to the suspicious lesion and its reaction was captured before and after application using smartphone endoscope. This screening system enables to extract the features of cancers and informs the possibility of cancer from endoscopic images.clos

Review of photoacoustic imaging plus X

Photoacoustic imaging (PAI) is a novel modality in biomedical imaging technology that combines the rich optical contrast with the deep penetration of ultrasound. To date, PAI technology has found applications in various biomedical fields. In this review, we present an overview of the emerging research frontiers on PAI plus other advanced technologies, named as PAI plus X, which includes but not limited to PAI plus treatment, PAI plus new circuits design, PAI plus accurate positioning system, PAI plus fast scanning systems, PAI plus novel ultrasound sensors, PAI plus advanced laser sources, PAI plus deep learning, and PAI plus other imaging modalities. We will discuss each technology's current state, technical advantages, and prospects for application, reported mostly in recent three years. Lastly, we discuss and summarize the challenges and potential future work in PAI plus X area

Ultrasound Imaging

This book provides an overview of ultrafast ultrasound imaging, 3D high-quality ultrasonic imaging, correction of phase aberrations in medical ultrasound images, etc. Several interesting medical and clinical applications areas are also discussed in the book, like the use of three dimensional ultrasound imaging in evaluation of Asherman's syndrome, the role of 3D ultrasound in assessment of endometrial receptivity and follicular vascularity to predict the quality oocyte, ultrasound imaging in vascular diseases and the fetal palate, clinical application of ultrasound molecular imaging, Doppler abdominal ultrasound in small animals and so on

Photoacoustic Imaging in Gastroenterology: Advances and Needs

Gastroenterologists routinely use optical imaging and ultrasound for the minimally invasive diagnosis and treatment of chronic inflammatory diseases and cancerous tumors in gastrointestinal tract and related organs. Recent advances in gastroenterological photoacoustics represent combination of multispectral and multiscale photoacoustic (PA), ultrasound (US), and near-infrared (NIR) fluorescent imaging. The novel PA endoscopic methods have been evaluated in preclinical models using catheter-based miniature probes either noncontact, all-optical, forward-viewing probe or contact, side-viewing probe combined with ultrasound (esophagus and colon). The deep-tissue PA tomography has been applied to preclinical research on targeted contrast agents (pancreatic cancer) using benchtop experimental setups. The clinical studies engaging human tissue ex vivo have been performed on endoscopic mucosal resection tissue with PA-US tomography system and intraoperative imaging of pancreatic tissue with PA and NIR fluorescence multimodality. These emerging PA methods are very promising for early cancer detection and prospective theranostics. The noninvasive transabdominal examination with PA-US handheld probe has been implemented into clinical trials for the assessment of inflammatory bowel disease. To facilitate translational and clinical research in PA imaging in gastroenterology, we discuss potential clinical impact and limitations of the proposed solutions and future needs

Advances in Ophthalmology

This book focuses on the different aspects of ophthalmology - the medical science of diagnosis and treatment of eye disorders. Ophthalmology is divided into various clinical subspecialties, such as cornea, cataract, glaucoma, uveitis, retina, neuro-ophthalmology, pediatric ophthalmology, oncology, pathology, and oculoplastics. This book incorporates new developments as well as future perspectives in ophthalmology and is a balanced product between covering a wide range of diseases and expedited publication. It is intended to be the appetizer for other books to follow. Ophthalmologists, researchers, specialists, trainees, and general practitioners with an interest in ophthalmology will find this book interesting and useful

Fast widefield techniques for fluorescence and phase endomicroscopy

Thesis (Ph.D.)--Boston UniversityEndomicroscopy is a recent development in biomedical optics which gives researchers and physicians microscope-resolution views of intact tissue to complement macroscopic visualization during endoscopy screening. This thesis presents HiLo endomicroscopy and oblique back-illumination endomicroscopy, fast widefield imaging techniques with fluorescence and phase contrast, respectively. Fluorescence imaging in thick tissue is often hampered by strong out-of-focus background signal. Laser scanning confocal endomicroscopy has been developed for optically-sectioned imaging free from background, but reliance on mechanical scanning fundamentally limits the frame rate and represents significant complexity and expense. HiLo is a fast, simple, widefield fluorescence imaging technique which rejects out-of-focus background signal without the need for scanning. It works by acquiring two images of the sample under uniform and structured illumination and synthesizing an optically sectioned result with real-time image processing. Oblique back-illumination microscopy (OBM) is a label-free technique which allows, for the first time, phase gradient imaging of sub-surface morphology in thick scattering tissue with a reflection geometry. OBM works by back-illuminating the sample with the oblique diffuse reflectance from light delivered via off-axis optical fibers. The use of two diametrically opposed illumination fibers allows simultaneous and independent measurement of phase gradients and absorption contrast. Video-rate single-exposure operation using wavelength multiplexing is demonstrated