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

Abstract

Optical coherence tomography (OCT) is an optical diagnostic modality that can acquire cross-sectional images of the microscopic structure of the esophagus, including Barrett's esophagus (BE) and associated dysplasia. We developed a swallowable tethered capsule OCT endomicroscopy (TCE) device that acquires high-resolution images of entire gastrointestinal (GI) tract luminal organs. This device has a potential to become a screening method that identifies patients with an abnormal esophagus that should be further referred for upper endoscopy. Currently, the characterization of the OCT-TCE esophageal wall data set is performed manually, which is time-consuming and inefficient. Additionally, since the capsule optics optimally focus light approximately 500 µm outside the capsule wall and the best quality images are obtained when the tissue is in full contact with the capsule, it is crucial to provide feedback for the operator about tissue contact during the imaging procedure. In this study, we developed a fully automated algorithm for the segmentation of in vivo OCT-TCE data sets and characterization of the esophageal wall. The algorithm provides a two-dimensional representation of both the contact map from the data collected in human clinical studies as well as a tissue map depicting areas of BE with or without dysplasia. Results suggest that these techniques can potentially improve the current TCE data acquisition procedure and provide an efficient characterization of the diseased esophageal wall.

Keywords: (100.6950) Tomographic image processing; (170.1610) Clinical applications; (170.2680) Gastrointestinal; (170.3880) Medical and biological imaging; (170.4500) Optical coherence tomography; (170.6935) Tissue characterization.

Figures

Fig. 1

The tether encloses an optical fiber that delivers light from the OCT system through the capsule. The capsule encloses micro optics capable of redirecting and focusing the light laterally and immediately outside of its housing for side viewing imaging. Cross sectional imaging is accomplished by spinning micro optics in the capsule and fiber proximally at a speed of 20 frames per second by the means of a rotary junction.

Fig. 2

Flowchart of the entire automated processing framework. The algorithm receives as its input an entire OCT pullback data set that typically comprises >1,000 images. Initially, the algorithm automatically locates the position of the tissue’s surface over the entire 3D data set and then tissue characteristics are assigned. Squamous esophagus is differentiated from BE (with or without dysplasia) tissue by identifying the presence of horizontal layers. The output of the algorithm is a tissue map of the entire data set, automatically depicting the presence of BE and a contact map showing areas that lack contact between the capsule and the tissue.

Fig. 3

An entire OCT data set can be displayed as a single image by concatenating adjacent frames. w indicates the window used for binarization and s is the rectangular structuring element used for morphological image dilation/erosion following binarization.

Fig. 4

Layer detection. (a) shows a representative OCT image of squamous esophagus where the typical layers are visible: squamous epithelium (E), lamina propria (L), muscularis mucosa (MM), submucosa (S), inner muscularis propria (IM) and outer muscularis propria (OM). (b) shows an example of BE tissue where the layers characterizing normal esophagus are absent. (c) schematic A-lines for both layered normal esophagus (from lumen to IM – blue line) and BE (red).. Blue arrow and vertical/horizontal lines correspond to peak position, height and width measured at half-height, respectively. Scale bar equal to 500 µm.

Fig. 5

Example of segmentation results and tissue contact analysis. (a) capsule in the stomach with very low tissue contact, (b) capsule in the esophagus with complete contact, (c) capsule partially touching the esophageal wall (d) 2D contact map automatically generated by segmenting the entire data set. Colormap ranges from blue to red, where blue corresponds to full contact and red corresponds to no contact. (e) example of segmentation with the presence of intraluminal debris (indicated by *). (f) and (g) additional examples of fully automated segmentation results.

Fig. 6

Example of tissue characterization results. (a)OCT cross sectional image of BE,(b)squamous and(c)lack of contact at 5 o’clock. (d) Tissue 2D map depicting BE tissue distribution. Yellow color indicates tissue classified as squamous esophagus, red color depicts BE (green arrow) and grey color corresponds to lack of contact. Blue arrow indicates folding artifact (see section 6.1).

Fig. 7

Examples of lack of contact. Image (a) shows an example of squamous esophagus and tissue out of capsule focus with reduced image contrast between the different layers (arrow). Image (b) shows an example of tissue out of the system and capsule image range (arrow).

Fig. 8

Appearance of squamous esophagus (a) compared to BE (b) and folded squamous tissue, or the so called “folding artifact” (FA). As it is possible to appreciate from the image, folded squamous esophagus appearance is similar to BE, as the layers that typify squamous mucosa disappear on the OCT image.