Aleksandar Juloski

Automatic region-of-interest segmentation and pathology detection in magnetically guided capsule endoscopy.

Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010 as a procedure where a capsule in the stomach is navigated via an external magnetic field. The quality of the examination depends on the operators ability to detect aspects of interest in real time. We present a novel two step computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis and gastrointestinal bleedings in the stomach during the examination. First, we identify and exclude subregions of bubbles which can interfere with further processing. Then we address the challenge of lesion localization in an environment with changing contrast and lighting conditions. After a contrast-normalized filtering, feature extraction is performed. The proposed algorithm was tested on 300 images of different patients with uniformly distributed occurrences of the target pathologies. We correctly segmented 84.72% of bubble areas. A mean detection rate of 86% for the target pathologies was achieved during a 5-fold leave-one-out cross-validation.

Semantic and topological classification of images in magnetically guided capsule endoscopy

Magnetically-guided capsule endoscopy (MGCE) is a nascent technology with the goal to allow the steering of a capsule endoscope inside a water filled stomach through an external magnetic field. We developed a classification cascade for MGCE images with groups images in semantic and topological categories. Results can be used in a post-procedure review or as a starting point for algorithms classifying pathologies. The first semantic classification step discards over-/under-exposed images as well as images with a large amount of debris. The second topological classification step groups images with respect to their position in the upper gastrointestinal tract (mouth, esophagus, stomach, duodenum). In the third stage two parallel classifications steps distinguish topologically different regions inside the stomach (cardia, fundus, pylorus, antrum, peristaltic view). For image classification, global image features and local texture features were applied and their performance was evaluated. We show that the third classification step can be improved by a bubble and debris segmentation because it limits feature extraction to discriminative areas only. We also investigated the impact of segmenting intestinal folds on the identification of different semantic camera positions. The results of classifications with a support-vector-machine show the significance of color histogram features for the classification of corrupted images (97%). Features extracted from intestinal fold segmentation lead only to a minor improvement (3%) in discriminating different camera positions.

Chromoendoscopy in magnetically guided capsule endoscopy

BackgroundDiagnosis of intestinal metaplasia and dysplasia via conventional endoscopy is characterized by low interobserver agreement and poor correlation with histopathologic findings. Chromoendoscopy significantly enhances the visibility of mucosa irregularities, like metaplasia and dysplasia mucosa. Magnetically guided capsule endoscopy (MGCE) offers an alternative technology for upper GI examination. We expect the difficulties of diagnosis of neoplasm in conventional endoscopy to transfer to MGCE. Thus, we aim to chart a path for the application of chromoendoscopy on MGCE via an ex-vivo animal study.MethodsWe propose a modified preparation protocol which adds a staining step to the existing MGCE preparation protocol. An optimal staining concentration is quantitatively determined for different stain types and pathologies. To that end 190 pig stomach tissue samples with and without lesion imitations were stained with different dye concentrations. Quantitative visual criteria are introduced to measure the quality of the staining with respect to mucosa and lesion visibility. Thusly determined optimal concentrations are tested in an ex-vivo pig stomach experiment under magnetic guidance of an endoscopic capsule with the modified protocol.ResultsWe found that the proposed protocol modification does not impact the visibility in the stomach or steerability of the endoscopy capsule. An average optimal staining concentration for the proposed protocol was found at 0.4% for Methylene blue and Indigo carmine. The lesion visibility is improved using the previously obtained optimal dye concentration.ConclusionsWe conclude that chromoendoscopy may be applied in MGCE and improves mucosa and lesion visibility. Systematic evaluation provides important information on appropriate staining concentration. However, further animal and human in-vivo studies are necessary.

On-the-fly Detection of Images with Gastritis Aspects in Magnetically-Guided Capsule Endoscopy

Capsule Endoscopy (CE) was introduced in 2000 and has since become an established diagnostic procedure for the small bowel, colon and esophagus. For the CE examination the patient swallows the capsule, which then travels through the gastrointestinal tract under the influence of the peristaltic movements. CE is not indicated for stomach examination, as the capsule movements can not be controlled from the outside and the entire surface of the stomach can not be reliably covered. Magnetically-guided capsule endoscopy (MGCE) was introduced in 2010. For the MGCE procedure the stomach is filled with water and the capsule is navigated from the outside using an external magnetic field. During the examination the operator can control the motion of the capsule in order to obtain a sufficient number of stomach-surface images with diagnostic value. The quality of the examination depends on the skill of the operator and his ability to detect aspects of interest in real time. We present a novel computer-assisted diagnostic-procedure (CADP) algorithm for indicating gastritis pathologies in the stomach during the examination. Our algorithm is based on pre-processing methods and feature vectors that are suitably chosen for the challenges of the MGCE imaging (suspended particles, bubbles, lighting). An image is classified using an ada-boost trained classifier. For the classifier training, a number of possible features were investigated. Statistical evaluation was conducted to identify relevant features with discriminative potential. The proposed algorithm was tested on 12 video sequences stemming from 6 volunteers. A mean detection rate of 91.17% was achieved during leave-one out cross-validation.