  Optical colonoscopy is an essential diagnostic and prognostic tool for many
gastrointestinal diseases, including cancer screening and staging, intestinal
bleeding, diarrhea, abdominal symptom evaluation, and inflammatory bowel
disease assessment. Automated assessment of colonoscopy is of interest
considering the subjectivity present in qualitative human interpretations of
colonoscopy findings. Localization of the camera is essential to interpreting
the meaning and context of findings for diseases evaluated by colonoscopy. In
this study, we propose a camera localization system to estimate the relative
location of the camera and classify the colon into anatomical segments. The
camera localization system begins with non-informative frame detection and
removal. Then a self-training end-to-end convolutional neural network is built
to estimate the camera motion, where several strategies are proposed to improve
its robustness and generalization on endoscopic videos. Using the estimated
camera motion a camera trajectory can be derived and a relative location index
calculated. Based on the estimated location index, anatomical colon segment
classification is performed by constructing a colon template. The proposed
motion estimation algorithm was evaluated on an external dataset containing the
ground truth for camera pose. The experimental results show that the
performance of the proposed method is superior to other published methods. The
relative location index estimation and anatomical region classification were
further validated using colonoscopy videos collected from routine clinical
practice. This validation yielded an average accuracy in classification of
0.754, which is substantially higher than the performances obtained using
location indices built from other methods.
