Deep principal dimension encoding for the classification of early neoplasia in Barretts Esophagus with volumetric laser endomicroscopy Abstract   Barrett cancer is a treatable disease when detected at an early stage. However, current screening protocols are often not effective at finding the disease early. Volumetric Laser Endomicroscopy (VLE) is a promising new imaging tool for finding dysplasia in Barrettu0027s esophagus (BE) at an early stage, by acquiring crosssectional images of the microscopic structure of BE up to 3mm deep. However, interpretation of VLE scans is difficult for medical doctors due to both the size and subtlety of the grayscale data. Therefore, algorithms that can accurately find cancerous regions are very valuable for the interpretation of VLE data. In this study, we propose a fullyautomatic multistep ComputerAided Detection (CAD) algorithm that optimally leverages the effectiveness of deep learning strategies by encoding the principal dimension in VLE data. Additionally, we show that combining the encoded dimensions with conventional machine learning techniques further improves results while maintaining interpretability. Furthermore, we train and validate our algorithm on a new histopathologically validated set of invivo VLE snapshots. Additionally, an independent test set is used to assess the performance of the model. Finally, we compare the performance of our algorithm against previous stateoftheart systems. With the encoded principal dimension, we obtain an Area Under the Curve (AUC) and F1 score of 0.93 and 87.4% on the test set respectively. We show this is a significant improvement compared to the stateoftheart of 0.89 and 83.1%, respectively, thereby demonstrating the effectiveness of our approach.

Neural Network Training Data Profoundly Impacts TextureBased Intravascular Image Segmentation Segmentation of variably differentiated and lowfrequency elements in a complex image is challenging. Improving sensitivity demands often prohibitive decreases in specificity. This is particularly the case in intravascular imaging, where detection of heterogeneously dispersed lesion elements, which are often less evident than normal structures, is essential. Modalities including optical coherence tomography (OCT) provide crosssectional images of coronary arteries that reveal atherosclerotic plaques. Manual plaque segmentation is time consuming and error prone; automated methods are quicker but dictate accuracy tradeoffs. We developed a neural networkbased method for automatic detection of calcified plaques in OCT images using texturebased features and examined how underlying training data distribution impacts sensitivity and predictive value. The method assesses each pixel, rather than a patch, as an independent unit, enabling precise control of training data distribution while simultaneously decreasing reliance on massive imaging datasets for training. Pixels from 30 manually annotated OCT images of calcified plaques were used to train the neural network. Several texture measures were computed for the local neighborhood of each pixel and used as inputs to a multilayered neural network. The ratio of pixels of each class in the training dataset was then varied and the resulting network performance was compared. Positive predictive value and sensitivity ranged from 0.69 to 0.77 and 0.35 to 0.86, respectively, as the ratio of noncalcified to calcified pixels varied from around 15 to 1, with inverse changes in specificity. The results clearly demonstrate that appropriately balanced data must be carefully curated with thoughtful consideration of the modelu0027s application and the clinical imperative being addressed.