Comparative assessment of feature extraction methods for visual odometry in wireless capsule endoscopy

Endoscopic image feature matching via motion consensus and global bilateral regression

BACKGROUND AND OBJECTIVE

Feature matching of endoscopic images is of crucial importance in many clinical applications, such as object tracking and surface reconstruction. However, with the presence of low texture, specular reflections and deformations, the feature matching methods of natural scene are facing great challenges in minimally invasive surgery (MIS) scenarios. We propose a novel motion consensus-based method for endoscopic image feature matching to address these problems.

METHODS

Our method starts by correcting the radial distortion with the spherical projection model and removing the specular reflection regions with an adaptive detection method, which helps to eliminate the image distortion and to reduce the quantity of outliers. We solve the matching problem with a two-stage strategy that progressively estimates a consensus of inliers; the result is a precisely smoothed motion field. First, we construct a spatial motion field from candidate feature matches and estimate its maximum posterior with expectation maximization algorithm, which is computationally efficient and able to obtain smoothed motion field quickly. Second, we extend the smoothed motion field to the affine domain and refine it with bilateral regression to preserve locally subtle motions. The true matches can be identified by checking the difference of feature motion against the estimated field.

RESULTS

Evaluations are implemented on two simulation datasets of deformation (218 images) and four different types of endoscopic datasets (1032 images). Our method is compared with three other state-of-the-art methods and achieves the best performance on affine transformation and nonrigid deformation simulations, with inlier ratio of 86.7% and 94.3%, sensitivity of 90.0% and 96.2%, precision of 88.2% and 93.9%, and F1-Score of 89.1% and 95.0%, respectively. On clinical datasets evaluations, the proposed method achieves an average reprojection error of 3.7 pixels and a consistent performance in multi-image correspondence of sequential images. Furthermore, we also present a surface reconstruction result from rhinoscopic images to validate the reliability of our method, which shows high-quality feature matching results.

CONCLUSIONS

The proposed motion consensus-based feature matching method is proved effective and robust for endoscopic images correspondence. This demonstrates its capability to generate reliable feature matches for surface reconstruction and other meaningful applications in MIS scenarios.

Follow-up on: optimizing lesion detection in small bowel capsule endoscopy and beyond: from present problems to future solutions

This review presents noteworthy advances in clinical and experimental Capsule Endoscopy (CE), focusing on the progress that has been reported over the last 5 years since our previous review on the subject. Areas covered: This study presents the commercially available CE platforms, as well as the advances made in optimizing the diagnostic capabilities of CE. The latter includes recent concept and prototype capsule endoscopes, medical approaches to improve diagnostic yield, and progress in software for enhancing visualization, abnormality detection, and lesion localization. Expert commentary: Currently, moving through the second decade of CE evolution, there are still several open issues and remarkable challenges to overcome.

Deep Endoscopic Visual Measurements

Robotic endoscopic systems offer a minimally invasive approach to the examination of internal body structures, and their application is rapidly extending to cover the increasing needs for accurate therapeutic interventions. In this context, it is essential for such systems to be able to perform measurements, such as measuring the distance traveled by a wireless capsule endoscope, so as to determine the location of a lesion in the gastrointestinal tract, or to measure the size of lesions for diagnostic purposes. In this paper, we investigate the feasibility of performing contactless measurements using a computer vision approach based on neural networks. The proposed system integrates a deep convolutional image registration approach and a multilayer feed-forward neural network into a novel architecture. The main advantage of this system, with respect to the state-of-the-art ones, is that it is more generic in the sense that it is 1) unconstrained by specific models, 2) more robust to nonrigid deformations, and 3) adaptable to most of the endoscopic systems and environment, while enabling measurements of enhanced accuracy. The performance of this system is evaluated under ex vivo conditions using a phantom experimental model and a robotically assisted test bench. The results obtained promise a wider applicability and impact in endoscopy in the era of big data.

Novel experimental and software methods for image reconstruction and localization in capsule endoscopy

BACKGROUND AND STUDY AIMS

Capsule endoscopy (CE) is invaluable for minimally invasive endoscopy of the gastrointestinal tract; however, several technological limitations remain including lack of reliable lesion localization. We present an approach to 3D reconstruction and localization using visual information from 2D CE images.

PATIENTS AND METHODS

Colored thumbtacks were secured in rows to the internal wall of a LifeLike bowel model. A PillCam SB3 was calibrated and navigated linearly through the lumen by a high-precision robotic arm. The motion estimation algorithm used data (light falling on the object, fraction of reflected light and surface geometry) from 2D CE images in the video sequence to achieve 3D reconstruction of the bowel model at various frames. The ORB-SLAM technique was used for 3D reconstruction and CE localization within the reconstructed model. This algorithm compared pairs of points between images for reconstruction and localization.

RESULTS

 As the capsule moved through the model bowel 42 to 66 video frames were obtained per pass. Mean absolute error in the estimated distance travelled by the CE was 4.1 ± 3.9 cm. Our algorithm was able to reconstruct the cylindrical shape of the model bowel with details of the attached thumbtacks. ORB-SLAM successfully reconstructed the bowel wall from simultaneous frames of the CE video. The "track" in the reconstruction corresponded well with the linear forwards-backwards movement of the capsule through the model lumen.

CONCLUSION

 The reconstruction methods, detailed above, were able to achieve good quality reconstruction of the bowel model and localization of the capsule trajectory using information from the CE video and images alone.