Automated segmentation and characterization of esophageal wall in vivo by tethered capsule optical coherence tomography endomicroscopy

Endoluminal Procedures and Devices for Esophageal Tract Investigation: A Critical Review

Diseases of the esophageal tract represent a heterogeneous class of pathological conditions for which diagnostic paradigms continue to emerge. In the last few decades, innovative diagnostic devices have been developed, and several attempts have been made to advance and standardize diagnostic algorithms to be compliant with medical procedures. To the best of our knowledge, a comprehensive review of the procedures and available technologies to investigate the esophageal tract was missing in the literature. Therefore, the proposed review aims to provide a comprehensive analysis of available endoluminal technologies and procedures to investigate esophagus health conditions. The proposed systematic review was performed using PubMed, Scopus, and Web of Science databases. Studies have been divided into categories based on the type of evaluation and measurement that the investigated technology provides. In detail, three main categories have been identified, i.e., endoluminal technologies for the (i) morphological, (ii) bio-mechanical, and (iii) electro-chemical evaluation of the esophagus.

Feasibility and Safety of Tethered Capsule Endomicroscopy in Patients With Barrett's Esophagus in a Multi-Center Study

BACKGROUND & AIMS

Tethered capsule endomicroscopy (TCE) involves swallowing a small tethered pill that implements optical coherence tomography (OCT) imaging, procuring high resolution images of the whole esophagus. Here, we demonstrate and evaluate the feasibility and safety of TCE and a portable OCT imaging system in patients with Barrett's esophagus (BE) in a multi-center (5-site) clinical study.

METHODS

Untreated patients with BE as per endoscopic biopsy diagnosis were eligible to participate in the study. TCE procedures were performed in unsedated patients by either doctors or nurses. After the capsule was swallowed, the device continuously obtained 10-μm-resolution cross-sectional images as it traversed the esophagus. Following imaging, the device was withdrawn through mouth, and disinfected for subsequent reuse. BE lengths were compared to endoscopy findings when available. OCT-TCE images were compared to volumetric laser endomicroscopy (VLE) images from a patient who had undergone VLE on the same day as TCE.

RESULTS

147 patients with BE were enrolled across all sites. 116 swallowed the capsule (79%), 95/114 (83.3%) men and 21/33 (63.6%) women (P = .01). High-quality OCT images were obtained in 104/111 swallowers (93.7%) who completed the procedure. The average imaging duration was 5.55 ± 1.92 minutes. The mean length of esophagus imaged per patient was 21.69 ± 5.90 cm. A blinded comparison of maximum extent of BE measured by OCT-TCE and EGD showed a strong correlation (r = 0.77-0.79). OCT-TCE images were of similar quality to those obtained by OCT-VLE.

CONCLUSIONS

The capabilities of TCE to be used across multiple sites, be administered to unsedated patients by either physicians or nurses who are not expert in OCT-TCE, and to rapidly and safely evaluate the microscopic structure of the esophagus make it an emerging tool for screening and surveillance of BE patients. Clinical trial registry website and trial number: NCT02994693 and NCT03459339.

Optical coherence tomography

Optical coherence tomography (OCT) is a non-contact method for imaging the topological and internal microstructure of samples in three dimensions. OCT can be configured as a conventional microscope, as an ophthalmic scanner, or using endoscopes and small diameter catheters for accessing internal biological organs. In this Primer, we describe the principles underpinning the different instrument configurations that are tailored to distinct imaging applications and explain the origin of signal, based on light scattering and propagation. Although OCT has been used for imaging inanimate objects, we focus our discussion on biological and medical imaging. We examine the signal processing methods and algorithms that make OCT exquisitely sensitive to reflections as weak as just a few photons and that reveal functional information in addition to structure. Image processing, display and interpretation, which are all critical for effective biomedical imaging, are discussed in the context of specific applications. Finally, we consider image artifacts and limitations that commonly arise and reflect on future advances and opportunities.

Connectivity-based deep learning approach for segmentation of the epithelium in in vivo human esophageal OCT images

Optical coherence tomography (OCT) is used for diagnosis of esophageal diseases such as Barrett's esophagus. Given the large volume of OCT data acquired, automated analysis is needed. Here we propose a bilateral connectivity-based neural network for in vivo human esophageal OCT layer segmentation. Our method, connectivity-based CE-Net (Bicon-CE), defines layer segmentation as a combination of pixel connectivity modeling and pixel-wise tissue classification. Bicon-CE outperformed other widely used neural networks and reduced common topological prediction issues in tissues from healthy patients and from patients with Barrett's esophagus. This is the first end-to-end learning method developed for automatic segmentation of the epithelium in in vivo human esophageal OCT images.

Advances in optical gastrointestinal endoscopy: a technical review

Optical endoscopy is the primary diagnostic and therapeutic tool for management of gastrointestinal (GI) malignancies. Most GI neoplasms arise from precancerous lesions; thus, technical innovations to improve detection and diagnosis of precancerous lesions and early cancers play a pivotal role in improving outcomes. Over the last few decades, the field of GI endoscopy has witnessed enormous and focused efforts to develop and translate accurate, user-friendly, and minimally invasive optical imaging modalities. From a technical point of view, a wide range of novel optical techniques is now available to probe different aspects of light-tissue interaction at macroscopic and microscopic scales, complementing white light endoscopy. Most of these new modalities have been successfully validated and translated to routine clinical practice. Herein, we provide a technical review of the current status of existing and promising new optical endoscopic imaging technologies for GI cancer screening and surveillance. We summarize the underlying principles of light-tissue interaction, the imaging performance at different scales, and highlight what is known about clinical applicability and effectiveness. Furthermore, we discuss recent discovery and translation of novel molecular probes that have shown promise to augment endoscopists' ability to diagnose GI lesions with high specificity. We also review and discuss the role and potential clinical integration of artificial intelligence-based algorithms to provide decision support in real time. Finally, we provide perspectives on future technology development and its potential to transform endoscopic GI cancer detection and diagnosis.

Dual-modality optical coherence tomography and fluorescence tethered capsule endomicroscopy

OCT tethered capsule endomicroscopy (TCE) is an emerging noninvasive diagnostic imaging technology for gastrointestinal (GI) tract disorders. OCT measures tissue reflectivity that provides morphologic image contrast, and thus is incapable of ascertaining molecular information that can be useful for improving diagnostic accuracy. Here, we introduce an extension to OCT TCE that includes a fluorescence (FL) imaging channel for attaining complementary, co-registered molecular contrast. We present the development of an OCT-FL TCE capsule and a portable, plug-and-play OCT-FL imaging system. The technology is validated in phantom experiments and feasibility is demonstrated in a methylene blue (MB)-stained swine esophageal injury model, ex vivo and in vivo.

Tissue self-attention network for the segmentation of optical coherence tomography images on the esophagus

Automatic segmentation of layered tissue is the key to esophageal optical coherence tomography (OCT) image processing. With the advent of deep learning techniques, frameworks based on a fully convolutional network are proved to be effective in classifying pixels on images. However, due to speckle noise and unfavorable imaging conditions, the esophageal tissue relevant to the diagnosis is not always easy to identify. An effective approach to address this problem is extracting more powerful feature maps, which have similar expressions for pixels in the same tissue and show discriminability from those from different tissues. In this study, we proposed a novel framework, called the tissue self-attention network (TSA-Net), which introduces the self-attention mechanism for esophageal OCT image segmentation. The self-attention module in the network is able to capture long-range context dependencies from the image and analyzes the input image in a global view, which helps to cluster pixels in the same tissue and reveal differences of different layers, thus achieving more powerful feature maps for segmentation. Experiments have visually illustrated the effectiveness of the self-attention map, and its advantages over other deep networks were also discussed.

Clinically Available Optical Imaging Technologies in Endoscopic Lesion Detection: Current Status and Future Perspective

Endoscopic optical imaging technologies for the detection and evaluation of dysplasia and early cancer have made great strides in recent decades. With the capacity of in vivo early detection of subtle lesions, they allow modern endoscopists to provide accurate and effective optical diagnosis in real time. This review mainly analyzes the current status of clinically available endoscopic optical imaging techniques, with emphasis on the latest updates of existing techniques. We summarize current coverage of these technologies in major hospital departments such as gastroenterology, urology, gynecology, otolaryngology, pneumology, and laparoscopic surgery. In order to promote a broader understanding, we further cover the underlying principles of these technologies and analyze their performance. Moreover, we provide a brief overview of future perspectives in related technologies, such as computer-assisted diagnosis (CAD) algorithms dealing with exploring endoscopic video data. We believe all these efforts will benefit the healthcare of the community, help endoscopists improve the accuracy of diagnosis, and relieve patients' suffering.

Progress in Screening for Barrett's Esophagus: Beyond Standard Upper Endoscopy

The rapid increase in the incidence of esophageal adenocarcinoma in Western populations over the past 4 decades and its associated poor prognosis, unless detected early has generated great interest in screening for the precursor lesion Barrett's esophagus (BE). Recently, there have been significant developments in imaging-based modalities and esophageal cell-sampling devices coupled with biomarker assays. In this review, the authors discuss the rationale for screening for BE and the factors to consider for targeting the at-risk population. They also explore future avenues for research in this area.

Advanced endoscopic imaging for detecting and guiding therapy of early neoplasias of the esophagus

Esophageal cancers, largely adenocarcinoma in Western countries and squamous cell cancer in Asia, present a significant burden of disease and remain one of the most lethal of cancers. Key to improving survival is the development and adoption of new imaging modalities to identify early neoplastic lesions, which may be small, multifocal, subsurface, and difficult to detect by standard endoscopy. Such advanced imaging is particularly relevant with the emergence of ablative techniques that often require multiple endoscopic sessions and may be complicated by bleeding, pain, strictures, and recurrences. Assessing the specific location, depth of involvement, and features correlated with neoplastic progression or incomplete treatment may optimize treatments. While not comprehensive of all endoscopic imaging modalities, we review here some of the recent advances in endoscopic luminal imaging, particularly with surface contrast enhancement using virtual chromoendoscopy, highly magnified subsurface imaging with confocal endomicroscopy, optical coherence tomography, elastic scattering spectroscopy, angle-resolved low-coherence interferometry, and light scattering spectroscopy. While there is no single ideal imaging modality, various multimodal instruments are also being investigated. The future of combining computer-aided assessments, molecular markers, and improved imaging technologies to help localize and ablate early neoplastic lesions shed hope for improved disease outcome.

Adversarial convolutional network for esophageal tissue segmentation on OCT images

Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.

Adversarial convolutional network for esophageal tissue segmentation on OCT images

Automatic segmentation is important for esophageal OCT image processing, which is able to provide tissue characteristics such as shape and thickness for disease diagnosis. Existing automatical segmentation methods based on deep convolutional networks may not generate accurate segmentation results due to limited training set and various layer shapes. This study proposed a novel adversarial convolutional network (ACN) to segment esophageal OCT images using a convolutional network trained by adversarial learning. The proposed framework includes a generator and a discriminator, both with U-Net alike fully convolutional architecture. The discriminator is a hybrid network that discriminates whether the generated results are real and implements pixel classification at the same time. Leveraging on the adversarial training, the discriminator becomes more powerful. In addition, the adversarial loss is able to encode high order relationships of pixels, thus eliminating the requirements of post-processing. Experiments on segmenting esophageal OCT images from guinea pigs confirmed that the ACN outperforms several deep learning frameworks in pixel classification accuracy and improves the segmentation result. The potential clinical application of ACN for detecting eosinophilic esophagitis (EoE), an esophageal disease, is also presented in the experiment.

Rapid head and neck tissue identification in thyroid and parathyroid surgery using optical coherence tomography

BACKGROUND

Optical coherence tomography (OCT) is a noninvasive imaging modality that may reproduce the microarchitecture of tissues in real-time. This study examines whether OCT can render distinct images of thyroid, parathyroid glands, adipose tissue, and lymph nodes in both healthy and pathological states.

METHODS

Twenty-seven patients undergoing thyroidectomy, parathyroidectomy, and/or neck dissection for thyroid cancer were recruited prospectively for imaging prior to histopathological analysis.

RESULTS

Based on 122 imaged specimens, qualitative OCT descriptions were derived for healthy thyroid, parathyroid gland, adipose tissue, and lymph node. The frequencies at which distinguishing features were present for each tissue type were 88%, 83%, 100%, and 82%. OCT appearance of pathological specimens were also described.

CONCLUSIONS

Healthy neck tissues have distinct OCT appearances, which could facilitate parathyroid identification during thyroidectomies. However, images of parathyroid adenomas could be confused with those of lymph nodes, and benign and malignant thyroid nodules could not be differentiated.

Imaging of cancer lipid metabolism in response to therapy

Lipids represent a diverse array of molecules essential to the cell's structure, defense, energy, and communication. Lipid metabolism can often become dysregulated during tumor development. During cancer therapy, targeted inhibition of cell proliferation can likewise cause widespread and drastic changes in lipid composition. Molecular imaging techniques have been developed to monitor altered lipid profiles as a biomarker for cancer diagnosis and treatment response. For decades, MRS has been the dominant non-invasive technique for studying lipid metabolite levels. Recent insights into the oncogenic transformations driving changes in lipid metabolism have revealed new mechanisms and signaling molecules that can be exploited using optical imaging, mass spectrometry imaging, and positron emission tomography. These novel imaging modalities have provided researchers with a diverse toolbox to examine changes in lipids in response to a wide array of anticancer strategies including chemotherapy, radiation therapy, signal transduction inhibitors, gene therapy, immunotherapy, or a combination of these strategies. The understanding of lipid metabolism in response to cancer therapy continues to evolve as each therapeutic method emerges, and this review seeks to summarize the current field and areas of unmet needs.

Wide-field optical property mapping and structured light imaging of the esophagus with spatial frequency domain imaging

As the incidence of esophageal adenocarcinoma continues to rise, there is a need for improved imaging technologies with contrast to abnormal esophageal tissues. To inform the design of optical technologies that meet this need, we characterize the spatial distribution of the scattering and absorption properties from 471 to 851 nm of eight resected human esophagi tissues using Spatial Frequency Domain Imaging. Histopathology was used to categorize tissue types, including normal, inflammation, fibrotic, ulceration, Barrett's Esophagus and squamous cell carcinoma. Average absorption and reduced scattering coefficients of normal tissues were 0.211 ± 0.051 and 1.20 ± 0.18 mm-1 , respectively at 471 nm, and both values decreased monotonically with increasing wavelength. Fibrotic tissue exhibited at least 68% larger scattering signal across all wavelengths, while squamous cell carcinoma exhibited a 36% decrease in scattering at 471 nm. We additionally image the esophagus with high spatial frequencies up to 0.5 mm-1 and show strong reflectance contrast to tissue treated with radiation. Lastly, we observe that esophageal absorption and scattering values change by an average of 9.4% and 2.7% respectively over a 30 minute duration post-resection. These results may guide system design for the diagnosis, prevention and monitoring of esophageal pathologies.

Gastrointestinal diagnosis using non-white light imaging capsule endoscopy

Capsule endoscopy (CE) has proved to be a powerful tool in the diagnosis and management of small bowel disorders since its introduction in 2001. However, white light imaging (WLI) is the principal technology used in clinical CE at present, and therefore, CE is limited to mucosal inspection, with diagnosis remaining reliant on visible manifestations of disease. The introduction of WLI CE has motivated a wide range of research to improve its diagnostic capabilities through integration with other sensing modalities. These developments have the potential to overcome the limitations of WLI through enhanced detection of subtle mucosal microlesions and submucosal and/or transmural pathology, providing novel diagnostic avenues. Other research aims to utilize a range of sensors to measure physiological parameters or to discover new biomarkers to improve the sensitivity, specificity and thus the clinical utility of CE. This multidisciplinary Review summarizes research into non-WLI CE devices by organizing them into a taxonomic structure on the basis of their sensing modality. The potential of these capsules to realize clinically useful virtual biopsy and computer-aided diagnosis (CADx) is also reported.

Ensemble of Deep Convolutional Neural Networks for Classification of Early Barrett’s Neoplasia Using Volumetric Laser Endomicroscopy

Barrett’s esopaghagus (BE) is a known precursor of esophageal adenocarcinoma (EAC). Patients with BE undergo regular surveillance to early detect stages of EAC. Volumetric laser endomicroscopy (VLE) is a novel technology incorporating a second-generation form of optical coherence tomography and is capable of imaging the inner tissue layers of the esophagus over a 6 cm length scan. However, interpretation of full VLE scans is still a challenge for human observers. In this work, we train an ensemble of deep convolutional neural networks to detect neoplasia in 45 BE patients, using a dataset of images acquired with VLE in a multi-center study. We achieve an area under the receiver operating characteristic curve (AUC) of 0.96 on the unseen test dataset and we compare our results with previous work done with VLE analysis, where only AUC of 0.90 was achieved via cross-validation on 18 BE patients. Our method for detecting neoplasia in BE patients facilitates future advances on patient treatment and provides clinicians with new assisting solutions to process and better understand VLE data.

Parallel deep neural networks for endoscopic OCT image segmentation

We report parallel-trained deep neural networks for automated endoscopic OCT image segmentation feasible even with a limited training data set. These U-Net-based deep neural networks were trained using a modified dice loss function and manual segmentations of ultrahigh-resolution cross-sectional images collected by an 800 nm OCT endoscopic system. The method was tested on in vivo guinea pig esophagus images. Results showed its robust layer segmentation capability with a boundary error of 1.4 µm insensitive to lay topology disorders. To further illustrate its clinical potential, the method was applied to differentiating in vivo OCT esophagus images from an eosinophilic esophagitis (EOE) model and its control group, and the results clearly demonstrated quantitative changes in the top esophageal layers' thickness in the EOE model.

Optical coherence tomography-guided laser marking with tethered capsule endomicroscopy in unsedated patients

Tethered capsule endomicroscopy (TCE) is an emerging screening technology that comprehensively obtains microstructural OCT images of the gastrointestinal (GI) tract in unsedated patients. To advance clinical adoption of this imaging technique, it will be important to validate TCE images with co-localized histology, the current diagnostic gold standard. One method for co-localizing OCT images with histology is image-targeted laser marking, which has previously been implemented using a driveshaft-based, balloon OCT catheter, deployed during endoscopy. In this paper, we present a TCE device that scans and targets the imaging beam using a low-cost stepper motor that is integrated inside the capsule. In combination with a 4-laser-diode, high power 1430/1450 nm marking laser system (800 mW on the sample and 1s pulse duration), this technology generated clearly visible marks, with a spatial targeting accuracy of better than 0.5 mm. A laser safety study was done on swine esophagus ex vivo, showing that these exposure parameters did not alter the submucosa, with a large, 4-5x safety margin. The technology was demonstrated in living human subjects and shown to be effective for co-localizing OCT TCE images to biopsies obtained during subsequent endoscopy.

Fast esophageal layer segmentation in OCT images of guinea pigs based on sparse Bayesian classification and graph search

Endoscopic optical coherence tomography (OCT) devices are capable of generating high-resolution images of esophageal structures at high speed. To make the obtained data easy to interpret and reveal the clinical significance, an automatic segmentation algorithm is needed. This work proposes a fast algorithm combining sparse Bayesian learning and graph search (termed as SBGS) to automatically identify six layer boundaries on esophageal OCT images. The SBGS first extracts features, including multi-scale gradients, averages and Gabor wavelet coefficients, to train the sparse Bayesian classifier, which is used to generate probability maps indicating boundary positions. Given these probability maps, the graph search method is employed to create the final continuous smooth boundaries. The segmentation performance of the proposed SBGS algorithm was verified by esophageal OCT images from healthy guinea pigs and the eosinophilic esophagitis (EoE) models. Experiments confirmed that the SBGS method is able to implement robust esophageal segmentation for all the tested cases. In addition, benefiting from the sparse model of SBGS, the segmentation efficiency is significantly improved compared to other widely used techniques.

Measuring Barrett's Epithelial Thickness with Volumetric Laser Endomicroscopy as a Biomarker to Guide Treatment

BACKGROUND

Radiofrequency ablation (RFA) treatment outcomes vary for unknown reasons. One hypothesis is that variations in Barrett's epithelial thickness (BET) are associated with reduced RFA efficacy for thicker BET and strictures for thinner BET. Volumetric laser endomicroscopy (VLE) is an imaging modality that acquires high-resolution, depth-resolved images of BE. However, the attenuation of light by tissue and the lack of layering in Barrett's tissue challenge BET measurements and the study of relationships between thickness and RFA outcomes. We aimed to quantify BET and compared the reliability of standard and contrast-enhanced VLE images.

METHODS

Baseline VLE scans from BE patients without prior ablative therapy and a Prague (M) length of > 1 cm were obtained from the US VLE Registry. An algorithm was applied to the VLE images to flatten the mucosal surface and enhance the contrast of different esophageal wall layers. Subsequently, BET was measured by two independent VLE readers using both contrast- and non-contrast-enhanced datasets. In order to validate these adjusted images, intra- and interobserver agreements were calculated.

RESULTS

VLE scans from fifty-seven patients were included in this study. BET was measured at eight equidistant locations on the selected cross-sectional images at 0.5 cm intervals from the GEJ to the proximal-most extent of BE. The intra-observer coefficients of the two readers for the contrast-enhanced images were 0.818 (95% CI 0.798-0.836) and 0.890 (95% CI 0.878-0.900). The interobserver agreement for the contrast-enhanced images (0.880; 95% CI 0.867-0.891) was significantly better than for the original images (0.778; 95% CI 0.754-0.799).

CONCLUSION

We developed an algorithm that improves VLE visualization of the mucosal layers of the esophageal wall and enables rapid and reliable measurement of BET. Interobserver variability measurements were significantly reduced when using contrast enhancement. Studies are underway to correlate BET with treatment response.

Computer-Aided Analysis of Gland-Like Subsurface Hyposcattering Structures in Barrett’s Esophagus Using Optical Coherence Tomography

(1) Background: Barrett’s esophagus (BE) is a complication of chronic gastroesophageal reflux disease and is a precursor to esophageal adenocarcinoma. The clinical implication of subsurface glandular structures of Barrett’s esophagus is not well understood. Optical coherence tomography (OCT), also known as volumetric laser endomicroscopy (VLE), can assess subsurface glandular structures, which appear as subsurface hyposcattering structures (SHSs). The aim of this study is to develop a computer-aided algorithm and apply it to investigate the characteristics of SHSs in BE using clinical VLE data; (2) Methods: SHSs were identified with an initial detection followed by machine learning. Comprehensive SHS characteristics including the number, volume, depth, size and shape were quantified. Clinical VLE datasets collected from 35 patients with a history of dysplasia undergoing BE surveillance were analyzed to study the general SHS distribution and characteristics in BE. A subset of radiofrequency ablation (RFA) patient data were further analyzed to investigate the pre-RFA SHS characteristics and post-RFA treatment response; (3) Results: SHSs in the BE region were significantly shallower, more vertical, less eccentric, and more regular, as compared with squamous SHSs. SHSs in the BE region which became neosquamous epithelium after RFA were shallower than those in the regions that remained BE. Pre-ablation squamous SHSs with higher eccentricity correlated strongly with larger reduction of post-ablation BE length for less elderly patients; (4) Conclusions: The computer algorithm is potentially a valuable tool for studying the roles of SHSs in BE.

Tethered capsule endomicroscopy for microscopic imaging of the esophagus, stomach, and duodenum without sedation in humans (with video)

BACKGROUND AND AIMS

Patients with many different digestive diseases undergo repeated EGDs throughout their lives. Tethered capsule endomicroscopy (TCE) is a less-invasive method for obtaining high-resolution images of the GI mucosa for diagnosis and treatment planning of GI tract diseases. In this article, we present our results from a single-center study aimed at testing the safety and feasibility of TCE for imaging the esophagus, stomach, and duodenum.

METHODS

After being swallowed by a participant without sedation, the tethered capsule obtains cross-sectional, 10 μm-resolution, optical coherence tomography images as the device traverses the alimentary tract. After imaging, the device is withdrawn through the mouth, disinfected, and reused. Safety and feasibility of TCE were tested, focusing on imaging the esophagus of healthy volunteers and patients with Barrett's esophagus (BE) and the duodenum of healthy volunteers. Images were compared with endoscopy and histopathology findings when available.

RESULTS

Thirty-eight patients were enrolled. No adverse effects were reported. The TCE device swallowing rate was 34 of 38 (89%). The appearance of a physiologic upper GI wall, including its microscopic pathology, was visualized with a tissue coverage of 85.4% ± 14.9% and 90.3% ± 6.8% in the esophagus of BE patients with and without endoscopic evidence of a hiatal hernia, respectively, as well as 84.8% ± 7.4% in the duodenum. A blinded comparison of TCE and endoscopic BE measurements showed a strong to very strong correlation (r = 0.7-0.83; P < .05) for circumferential extent and a strong correlation (r = 0.77-0.78; P < .01) for maximum extent (Prague classification). TCE interobserver correlation was very strong, at r = 0.92 and r = 0.84 (P < .01), for Prague classification circumferential (C) and maximal (M) length measurements, respectively.

CONCLUSIONS

TCE is a safe and feasible procedure for obtaining high-resolution microscopic images of the upper GI tract without endoscopic assistance or sedation.

Advanced Imaging for Barrett's Esophagus and Early Neoplasia: Surface and Subsurface Imaging for Diagnosis and Management

PURPOSE OF REVIEW

Esophageal adenocarcinoma bears one of the fastest rising incidence of any cancers and generally arises in the setting of gastroesophageal reflux and Barrett's esophagus. However, early detection of neoplasia can be challenging since most patients are asymptomatic until they progress to more advanced and less curable stages, and early dysplastic lesions can be small, multifocal, and difficult to detect. Clearly, new imaging tools are needed in light of sampling error associated with random biopsies, the current standard of practice.

RECENT FINDINGS

Advances in endoscopic imaging including virtual chromoendoscopy, confocal laser endomicroscopy, and subsurface imaging with optical coherence tomography have ushered in a new era for detecting subtle neoplastic lesions. Moreover, in light of esophagus-sparing treatments for neoplastic lesions, such tools are likely to guide ablation and follow-up management. While there is no ideal single imaging modality to facilitate improved detection, staging, ablation, and follow-up of patients with dysplastic Barrett's esophagus, new advances in available technology, the potential for multimodal imaging, and the use of computer-aided diagnosis and biomarkers all hold great promise for improving detection and treatment.

Robust layer segmentation of esophageal OCT images based on graph search using edge-enhanced weights

Automatic segmentation of esophageal layers in OCT images is crucial for studying esophageal diseases and computer-assisted diagnosis. This work aims to improve the current techniques to increase the accuracy and robustness for esophageal OCT image segmentation. A two-step edge-enhanced graph search (EEGS) framework is proposed in this study. Firstly, a preprocessing scheme is applied to suppress speckle noise and remove the disturbance in the esophageal structure. Secondly, the image is formulated into a graph and layer boundaries are located by graph search. In this process, we propose an edge-enhanced weight matrix for the graph by combining the vertical gradients with a Canny edge map. Experiments on esophageal OCT images from guinea pigs demonstrate that the EEGS framework is more robust and more accurate than the current segmentation method. It can be potentially useful for the early detection of esophageal diseases.

Current and Future Use of Esophageal Capsule Endoscopy

Capsule endoscopy can be a diagnostic option for patients with esophageal diseases who cannot tolerate esophagogastroduodenoscopy.Functional modifications of the capsule allow for thorough examination of the esophagus. Esophageal capsule endoscopy has so farfailed to show sufficient performance to justify the replacement of traditional endoscopy for the diagnosis of esophageal diseasesbecause the esophagus has a short transit time and common pathologies appear near the esophagogastric junction. However,technological improvements are being introduced to overcome the limitations of capsule endoscopy, which is expected to become agood alternative to conventional endoscopy.

Recent advances in Barrett's esophagus

Barrett's esophagus (BE) is the only known precursor of esophageal adenocarcinoma, one of the few cancers with increasing incidence in developed countries. The pathogenesis of BE is unclear with regard to either the cellular origin of this metaplastic epithelium or the manner in which malignant transformation occurs, although recent data indicate a possible junctional origin of stem cells for BE. Treatment of BE may be achieved using endoscopic eradication therapy; however, there is a lack of discriminatory tools to identify individuals at sufficient risk for cancer development in whom intervention is warranted. Reduction in gastroesophageal reflux of gastric contents including acid is mandatory to achieve remission from BE after endoscopic ablation, and can be achieved using medical or nonmedical interventions. Research topics of greatest interest include the mechanism of BE development and transformation to cancer, risk stratification methods to identify individuals who may benefit from ablation of BE, optimization of eradication therapy, and surveillance methods to ensure that remission is maintained after eradication is achieved.

Novel Developments in Endoscopic Mucosal Imaging

Endoscopic techniques such as high-definition and optical-chromoendoscopy have had enormous impact on endoscopy practice. Since these techniques allow assessment of most subtle morphological mucosal abnormalities, further improvements in endoscopic practice lay in increasing the detection efficacy of endoscopists. Several new developments could assist in this. First, web based training tools could improve the skills of the endoscopist for enhancing the detection and classification of lesions. Secondly, incorporation of computer aided detection will be the next step to raise endoscopic quality of the captured data. These systems will aid the endoscopist in interpreting the increasing amount of visual information in endoscopic images providing real-time objective second reading. In addition, developments in the field of molecular imaging open opportunities to add functional imaging data, visualizing biological parameters, of the gastrointestinal tract to white-light morphology imaging. For the successful implementation of abovementioned techniques, a true multi-disciplinary approach is of vital importance.

Emerging optical methods for endoscopic surveillance of Barrett's oesophagus

Barrett's oesophagus is an acquired metaplastic condition that predisposes patients to the development of oesophageal adenocarcinoma, prompting the use of surveillance regimes to detect early malignancy for endoscopic therapy with curative intent. The currently accepted surveillance regime uses white light endoscopy together with random biopsies, but has poor sensitivity and discards information from numerous light-tissue interactions that could be exploited to probe structural, functional, and molecular changes in the tissue. Advanced optical methods are now emerging that are highly sensitive to these changes and hold potential to improve surveillance of Barrett's oesophagus if they can be applied endoscopically. The next decade will see some of these exciting new methods applied to surveillance of Barrett's oesophagus in new device architectures for the first time, potentially leading to a long-awaited improvement in the standard of care.

Predictive features for early cancer detection in Barrett's esophagus using Volumetric Laser Endomicroscopy

The incidence of Barrett cancer is increasing rapidly and current screening protocols often miss the disease at an early, treatable stage. Volumetric Laser Endomicroscopy (VLE) is a promising new tool for finding this type of cancer early, capturing a full circumferential scan of Barrett's Esophagus (BE), up to 3-mm depth. However, the interpretation of these VLE scans can be complicated, due to the large amount of cross-sectional images and the subtle grayscale variations. Therefore, algorithms for automated analysis of VLE data can offer a valuable contribution to its overall interpretation. In this study, we broadly investigate the potential of Computer-Aided Detection (CADe) for the identification of early Barrett's cancer using VLE. We employ a histopathologically validated set of ex-vivo VLE images for evaluating and comparing a considerable set of widely-used image features and machine learning algorithms. In addition, we show that incorporating clinical knowledge in feature design, leads to a superior classification performance and additional benefits, such as low complexity and fast computation time. Furthermore, we identify an optimal tissue depth for classification of 0.5-1.0 mm, and propose an extension to the evaluated features that exploits this phenomenon, improving their predictive properties for cancer detection in VLE data. Finally, we compare the performance of the CADe methods with the classification accuracy of two VLE experts. With a maximum Area Under the Curve (AUC) in the range of 0.90-0.93 for the evaluated features and machine learning methods versus an AUC of 0.81 for the medical experts, our experiments show that computer-aided methods can achieve a considerably better performance than trained human observers in the analysis of VLE data.

Endoscopic Management of Early Adenocarcinoma and Squamous Cell Carcinoma of the Esophagus: Screening, Diagnosis, and Therapy

Because the esophagus is easily accessible with endoscopy, early diagnosis and curative treatment of esophageal cancer is possible. However, diagnosis is often delayed because symptoms are not specific during early stages of tumor development. The onset of dysphagia is associated with advanced disease, which has a survival at 5 years lower than 15%. Population screening by endoscopy is not cost-effective, but a number of alternative imaging and cell analysis technologies are under investigation. The ideal screening test should be inexpensive, well tolerated, and applicable to primary care. Over the past 10 years, significant progress has been made in endoscopic diagnosis and treatment of dysplasia (squamous and Barrett's), and early esophageal cancer using resection and ablation technologies supported by evidence from randomized controlled trials. We review the state-of-the-art technologies for early diagnosis and minimally invasive treatment, which together could reduce the burden of disease.

Review of optical coherence tomography in oncology

The application of optical coherence tomography (OCT) in the field of oncology has been prospering over the past decade. OCT imaging has been used to image a broad spectrum of malignancies, including those arising in the breast, brain, bladder, the gastrointestinal, respiratory, and reproductive tracts, the skin, and oral cavity, among others. OCT imaging has initially been applied for guiding biopsies, for intraoperatively evaluating tumor margins and lymph nodes, and for the early detection of small lesions that would often not be visible on gross examination, tasks that align well with the clinical emphasis on early detection and intervention. Recently, OCT imaging has been explored for imaging tumor cells and their dynamics, and for the monitoring of tumor responses to treatments. This paper reviews the evolution of OCT technologies for the clinical application of OCT in surgical and noninvasive interventional oncology procedures and concludes with a discussion of the future directions for OCT technologies, with particular emphasis on their applications in oncology.

Optical coherence tomography in gastroenterology: a review and future outlook

Optical coherence tomography (OCT) is an imaging technique optically analogous to ultrasound that can generate depth-resolved images with micrometer-scale resolution. Advances in fiber optics and miniaturized actuation technologies allow OCT imaging of the human body and further expand OCT utilization in applications including but not limited to cardiology and gastroenterology. This review article provides an overview of current OCT development and its clinical utility in the gastrointestinal tract, including disease detection/differentiation and endoscopic therapy guidance, as well as a discussion of its future applications.

Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study

Optical coherence tomography (OCT) yields microscopic volumetric images representing tissue structures based on the contrast provided by elastic light scattering. Multipatient studies using OCT for detection of tissue abnormalities can lead to large datasets making quantitative and unbiased assessment of classification algorithms performance difficult without the availability of automated analytical schemes. We present a mathematical descriptor reducing the dimensionality of a classifier's input data, while preserving essential volumetric features from reconstructed three-dimensional optical volumes. This descriptor is used as the input of classification algorithms allowing a detailed exploration of the features space leading to optimal and reliable classification models based on support vector machine techniques. Using imaging dataset of paraffin-embedded tissue samples from 38 ovarian cancer patients, we report accuracies for cancer detection [Formula: see text] for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating [Formula: see text] accuracy for the detection of high-grade serous, endometroid, and clear cells cancers. The classification approach reduces the computational complexity and needed resources to achieve highly accurate classification, making it possible to contemplate other applications, including intraoperative surgical guidance, as well as other depth sectioning techniques for fresh tissue imaging.

Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography

We propose a novel suite of algorithms for automatically segmenting the airway lumen and mucus in endobronchial optical coherence tomography (OCT) data sets, as well as a novel approach for quantifying the contents of the mucus. Mucus and lumen were segmented using a robust, multi-stage algorithm that requires only minimal input regarding sheath geometry. The algorithm performance was highly accurate in a wide range of airway and noise conditions. Mucus was classified using mean backscattering intensity and grey level co-occurrence matrix (GLCM) statistics. We evaluated our techniques in vivo in asthmatic and non-asthmatic volunteers.

Morphologic three-dimensional scanning of fallopian tubes to assist ovarian cancer diagnosis

The majority of high-grade serous ovarian cancers is now believed to originate in the fallopian tubes. Therefore, current practices include the pathological examination of excised fallopian tubes. Detection of tumors in the fallopian tubes using current clinical approaches remains difficult but is of critical importance to achieve accurate staging and diagnosis. Here, we present an intraoperative imaging system for the detection of human fallopian tube lesions. The system is based on optical coherence tomography (OCT) to access subepithelial tissue architecture. To demonstrate that OCT could identify lesions, we analyzed 180 OCT volumes taken from five different ovarian lesions and from healthy fallopian tubes, and compared them to standard pathological review. We demonstrated that qualitative features could be matched to pathological conditions. We then determined the feasibility of intraluminal imaging of intact human fallopian tubes by building a dedicated endoscopic single-fiber OCT probe to access the mucosal layer inside freshly excised specimens from five patients undergoing prophylactic surgeries. The probe insertion into the lumen acquired images over the entire length of the tubes without damaging the mucosa, providing the first OCT images of intact human fallopian tubes.

Recent advances in pediatric gastrointestinal endoscopy: an overview

Pediatric gastrointestinal endoscopy has become a fundamental component of health care for infants and children. New imaging technologies and creative extraluminal applications have brought exciting and clinically important benefits to pediatric gastrointestinal endoscopy. Areas covered: The impact of different new technologies in pediatric endoscopy and focused on improvements in mucosa visualization and the application of new noninvasive tools and procedures to avoid biopsies or surgery are reviewed. Expert commentary: Enhancement in mucosal visualization and reduction of anesthesia and biopsies are the main goals that guide the endoscopy development in pediatrics. The advent of newer imaging modalities has allowed clinicians to characterize and evaluate subtle mucosal lesions better, while advancements in current endoscopes have created the opportunity to monitor chronic conditions noninvasively. Continued expansion of these modalities seems certain, with increased utilization in pediatric gastroenterology.

The ecosystem that powered the translation of OCT from fundamental research to clinical and commercial impact [Invited]

25 years is a relatively short period of time for a medical technology to become a standard of care impacting the treatment of millions of people every year. Yet 25 years ago there were no OCT companies, no OCT products, no OCT markets, and only one journal article published using the term OCT (optical coherence tomography). OCT has had a tremendous scientific, clinical, and economic impact on society. Today, it is estimated that there are ~30 Million OCT imaging procedures performed worldwide every year and the OCT system market is approaching $1B per year. OCT has helped diagnose patients with retinal disease at early treatable stages, preventing or greatly reducing irreversible vision loss. The technology has facilitated pharmaceutical development and contributed to fundamental understanding of disease mechanisms in multiple fields. The invention and translation of OCT from fundamental research to daily clinical practice would not have been possible without a complex ecosystem involving interaction among physics, engineering, and clinical medicine; government funding of fundamental and clinical research; collaborative and competitive research in the academic sector; entrepreneurship and industry; addressing real clinical needs; harnessing the innovation that occurs at the boundaries of disciplines; and economic and societal impact. This invited review paper discusses the translation of OCT from fundamental research to clinical practice and commercial impact, as well as describes the ecosystem that helped power OCT to where it is today and will continue to drive future advances. While OCT is an example of a technology that has had a powerful impact, there are many biomedical technologies which are poised for translation to clinical practice, and it is our hope that highlighting this ecosystem will help accelerate their translation and clinical impact.

Accuracy and efficiency of computer-aided anatomical analysis using 3D visualization software based on semi-automated and automated segmentations

OBJECTIVE

We investigated and compared the functionality of two 3D visualization software provided by a CT vendor and a third-party vendor, respectively. Using surgical anatomical measurement as baseline, we evaluated the accuracy of 3D visualization and verified their utility in computer-aided anatomical analysis.

METHODS

The study cohort consisted of 50 adult cadavers fixed with the classical formaldehyde method. The computer-aided anatomical analysis was based on CT images (in DICOM format) acquired by helical scan with contrast enhancement, using a CT vendor provided 3D visualization workstation (Syngo) and a third-party 3D visualization software (Mimics) that was installed on a PC. Automated and semi-automated segmentations were utilized in the 3D visualization workstation and software, respectively. The functionality and efficiency of automated and semi-automated segmentation methods were compared. Using surgical anatomical measurement as a baseline, the accuracy of 3D visualization based on automated and semi-automated segmentations was quantitatively compared.

RESULTS

In semi-automated segmentation, the Mimics 3D visualization software outperformed the Syngo 3D visualization workstation. No significant difference was observed in anatomical data measurement by the Syngo 3D visualization workstation and the Mimics 3D visualization software (P>0.05).

CONCLUSIONS

Both the Syngo 3D visualization workstation provided by a CT vendor and the Mimics 3D visualization software by a third-party vendor possessed the needed functionality, efficiency and accuracy for computer-aided anatomical analysis.

Automated classification of optical coherence tomography images of human atrial tissue

Tissue composition of the atria plays a critical role in the pathology of cardiovascular disease, tissue remodeling, and arrhythmogenic substrates. Optical coherence tomography (OCT) has the ability to capture the tissue composition information of the human atria. In this study, we developed a region-based automated method to classify tissue compositions within human atria samples within OCT images. We segmented regional information without prior information about the tissue architecture and subsequently extracted features within each segmented region. A relevance vector machine model was used to perform automated classification. Segmentation of human atrial ex vivo datasets was correlated with trichrome histology and our classification algorithm had an average accuracy of 80.41% for identifying adipose, myocardium, fibrotic myocardium, and collagen tissue compositions.

Automated Internal Classification of Beadless Chinese ZhuJi Freshwater Pearls based on Optical Coherence Tomography Images

Optical coherence tomography (OCT) has been applied to inspect the internal defect of beadless Chinese ZhuJi fleshwater pearls. A novel fully automated algorithm is proposed to classify between normal and defective sub-layer in nacre layer. Our algorithm utilizes the graph segmentation approach to estimate the up and down boundaries of defect sub-layers from flattened and cropped image, and also proposes the strategy for edge and weight construction in segmentation process. The vertical gradients of boundary pixels are used to make grading decision. The algorithm is tested by typical pearl samples, and achieves 100% classification accuracy. The experiment result shows the feasibility and adaptability of the proposed approach, and proves that the OCT technique combined with proposed algorithm is a potential tool for fast and non-destructive diagnosis of internal structure of beadless pearl.

Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images

We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed “adjusted mean arc length” (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra’s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.