## 1 Introduction  
Esophageal adenocarcinoma (EAC) is among the most common and lethal cancers in the world. EAC has shown a rapid increase since the late 1980s and it is estimated that the number of new esophageal cancer cases will be doubled by 2030 [1]. Barrett's esophagus (BE) is a condition in which normal squamous epithelium at the distal end of the esophagus is replaced by metaplastic columnar epithelium due to overexposure to gastric acid and it is associated with an increased risk of developing EAC [2]. For this reason, patients diagnosed with BE currently undergo regular surveillance with white-light endoscopy (WLE) with the aim to detect early high-grade dysplasia (HGD) and intramucosal adenocarcinoma. It is important to detect these lesions early, as curative treatment is still possible at this stage by a minor endoscopic intervention. However, early neoplastic lesionsare regularly missed because of their subtle appearances, or as a result of sampling errors during biopsy [3; 4; 5]. Volumetric laser endomicroscopy (VLE) is a novel advanced imaging system with the potential to early detect suspicious areas containing BE, which may be regularly missed with current white-light endoscopy. VLE incorporates a second-generation form of optical coherence tomography (OCT) technology. Improvements in image acquisition speed enable this balloon-based system to perform a quick circumferential scan of the entire distal esophagus. VLE provides a three-dimensional map of near-microscopic resolution of the subsurface layers of the esophagus over a length of 6 cm and depth of 3 mm into the tissue. However, a VLE scan generates a large amount of gray-shaded data (i.e., typically 1200 cross-sectional images or frames of 4096 \(\times\) 2048 pixels) that need to be analyzed in real time during endoscopy. In recent studies [6; 7], clinical VLE prediction models were developed for BE neoplasia with successful accuracy results. Several visual VLE features (lack of layering, high surface signal intensity, and irregular glandular architecture) were identified as possible indicators of BE neoplasia by comparing VLE-histology correlated images from 25 ex-vivo specimens. However, follow-up studies using ex-vivo [8] and in-vivo [9] VLE data suggested that full-scan VLE interpretation by experts remains a challenge. Computer aided diagnosis (CADx) systems offer relevant assistance in clinical decision making. In [PERSON] et al. [10], a CADx system was developed for the detection of early BE neoplasia on WLE images. Early work in endoscopic optical coherence tomography (EOCT) was shown in [PERSON] et al. [11; 12], where a CADx system based on multiple feature extraction methods was developed to detect neoplasia in BE patients in a dataset of EOCT biopsies. Similar related work was performed by [PERSON] et al. [13], where a system was developed to automatically characterize and segment the esophageal wall of patients with BE using tethered capsule endomicroscopy (TCE). The first related work performed on VLE was presented in [PERSON] et al. [14], where a CADx system was developed to detect early BE neoplasia on 60 VLE images from a database of high-quality ex-vivo VLE-histology correlations (30 non-dysplastic BE (NDBE) and 30 neoplastic images, containing HGD or early EAC). In their work, two novel clinically-inspired quantitative image features specific for VLE were developed based on the VLE surface signal and the intensity histogram of several layers. Recently, additional work was performed by [PERSON] et al. [15], where another novel clinically-inspired quantitative image feature was developed based on the glandular architecture, and analyzed in a dataset of 18 BE patients with and without early BE neoplasia (88 NDBE and 34 HGD/EAC). Both studies investigated the features using several machine learning methods, such as support vector machine, random forest or AdaBoost, and showed successful results towards BE neoplasia assessment.