Combining Deep Learning with Geometric Features for Image based Localization in the Gastrointestinal Tract

TL;DR

This paper introduces a hybrid approach combining deep learning and geometric features for improved monocular colonoscope localization in the GI tract, addressing limited training data issues.

Contribution

It proposes a novel Siamese network-based few-shot classification combined with geometric pose estimation for better localization with small datasets.

Findings

Achieves 28.94% improvement in position accuracy

Achieves 10.97% improvement in orientation accuracy

Significantly outperforms traditional and deep learning methods

Abstract

Tracking monocular colonoscope in the Gastrointestinal tract (GI) is a challenging problem as the images suffer from deformation, blurred textures, significant changes in appearance. They greatly restrict the tracking ability of conventional geometry based methods. Even though Deep Learning (DL) can overcome these issues, limited labeling data is a roadblock to state-of-art DL method. Considering these, we propose a novel approach to combine DL method with traditional feature based approach to achieve better localization with small training data. Our method fully exploits the best of both worlds by introducing a Siamese network structure to perform few-shot classification to the closest zone in the segmented training image set. The classified label is further adopted to initialize the pose of scope. To fully use the training dataset, a pre-generated triangulated map points within the zone in the training set are registered with observation and contribute to estimating the optimal pose of the test image. The proposed hybrid method is extensively tested and compared with existing methods, and the result shows significant improvement over traditional geometric based or DL based localization. The accuracy is improved by 28.94% (Position) and 10.97% (Orientation) with respect to state-of-art method.