A novel clinical gland feature for detection of early Barrett’s neoplasia using volumetric laser endomicroscopy

Volumetric laser endomicroscopy (VLE) is an advanced imaging system offering a promising solution for the detection of early Barrett’s esophagus (BE) neoplasia. BE is a known precursor lesion for esophageal adenocarcinoma and is often missed during regular endoscopic surveillance of BE patients. VLE provides a circumferential scan of near-microscopic resolution of the esophageal wall up to 3-mm depth, yielding a large amount of data that is hard to interpret in real time. In a preliminary study on an automated analysis system for ex vivo VLE scans, novel quantitative image features were developed for two previously identified clinical VLE features predictive for BE neoplasia, showing promising results. This paper proposes a novel quantitative image feature for a missing third clinical VLE feature. The novel gland-based image feature called “gland statistics” (GS), is compared to several generic image analysis features and the most promising clinically-inspired feature “layer histogram” (LH). All features are evaluated on a clinical, validated data set consisting of 88 non-dysplastic BE and 34 neoplastic in vivo VLE images for eight different widely-used machine learning methods. The new clinically-inspired feature has on average superior classification accuracy (0.84 AUC) compared to the generic image analysis features (0.61 AUC), as well as comparable performance to the LH feature (0.86 AUC). Also, the LH feature achieves superior classification accuracy compared to the generic image analysis features in vivo, confirming previous ex vivo results. Combining the LH and the novel GS features provides even further improvement of the performance (0.88 AUC), showing great promise for the clinical utility of this algorithm to detect early BE neoplasia

Advanced Imaging and Sampling in Barrett's Esophagus: Artificial Intelligence to the Rescue?

Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neoplastic lesions, many new endoscopic imaging and sampling techniques have been developed. None of these techniques, however, have significantly increased the diagnostic yield of BE neoplasia. In fact, these techniques have led to an increase in the amount of visible information, yet endoscopists and pathologists inevitably suffer from variations in intra- and interobserver agreement. Artificial intelligence systems have the potential to overcome these endoscopist-dependent limitations

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

Blue-light imaging and linked-color imaging improve visualization of Barrett's neoplasia by nonexpert endoscopists

BACKGROUND AND AIMS: Endoscopic recognition of early Barrett's neoplasia is challenging. Blue-light imaging (BLI) and linked-color imaging (LCI) may assist endoscopists in appreciation of neoplasia. Our aim was to evaluate BLI and LCI for visualization of Barrett's neoplasia in comparison with white-light endoscopy (WLE) alone, when assessed by nonexpert endoscopists. METHODS: In this web-based assessment, corresponding WLE, BLI, and LCI images of 30 neoplastic Barrett's lesions were delineated by 3 expert endoscopists to establish ground truth. These images were then scored and delineated by 76 nonexpert endoscopists from 3 countries and with different levels of expertise, in 4 separate assessment phases with a washout period of 2 weeks. Assessments were as follows: assessment 1, WLE only; assessment 2, WLE + BLI; assessment 3, WLE + LCI; assessment 4, WLE + BLI + LCI. The outcomes were (1) appreciation of macroscopic appearance and ability to delineate lesions (visual analog scale [VAS] scores); (2) preferred technique (ordinal scores); and (3) assessors' delineation performance in terms of overlap with expert ground truth. RESULTS: Median VAS scores for phases 2 to 4 were significantly higher than in phase 1 (P < .001). Assessors preferred BLI and LCI over WLE for appreciation of macroscopic appearance (P < .001) and delineation (P < .001). Linear mixed-effect models showed that delineation performance increased significantly in phase 4. CONCLUSIONS: The use of BLI and LCI has significant additional value for the visualization of Barrett's neoplasia when used by nonexpert endoscopists. Assessors appreciated the addition of BLI and LCI better than the use of WLE alone. Furthermore, this addition led to improved delineation performance, thereby allowing for better acquisition of targeted biopsy samples. (The Netherlands Trial Registry number: NL7541.).status: publishe

Advanced Imaging and Sampling in Barrett's Esophagus: Artificial Intelligence to the Rescue?

Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neoplastic lesions, many new endoscopic imaging and sampling techniques have been developed. None of these techniques, however, have significantly increased the diagnostic yield of BE neoplasia. In fact, these techniques have led to an increase in the amount of visible information, yet endoscopists and pathologists inevitably suffer from variations in intra- and interobserver agreement. Artificial intelligence systems have the potential to overcome these endoscopist-dependent limitations

Tissue-border detection in volumetric laser endomicroscopy using bi-directional gated recurrent neural networks

Computer-aided detection (CAD) approaches have shown promising results for early esophageal cancer detection using Volumetric Laser Endoscopy (VLE) imagery. However, the relatively slow and computationally costly tissue segmentation employed in these approaches hamper their clinical applicability. In this paper, we propose to reframe the 2D tissue segmentation problem into a 1D tissue boundary detection problem. Instead of using an encoder-decoder architecture, we propose to follow the tissue boundary using a Recurrent Neural Network (RNN), exploiting the spatio-temporal relations within VLE frames. We demonstrate a near state-of-the-art performance using 18 times less floating point operations, enabling real-time execution in clinical practice.</p