  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.
