Robotizing flexible endoscopy enables image-based control of endoscopes. Especially during high-throughput procedures, such as a colonoscopy, navigation support algorithms could improve procedure turnaround and ergonomics for the endoscopist.
In this study, we have developed and implemented a navigation algorithm that is based on image classification followed by dark region segmentation. Robustness and accuracy were evaluated on real images obtained from human colonoscopy exams. Comparison was done using manual annotation as a reference. Intraclass correlation (ICC) was employed as a measure for similarity between automated and manual results.
The discrimination of the developed classifier was 6.8, making it a reliable classifier. In the experiments, the developed algorithm gave an ICC of 93 % (range 84.7–98.8 %) over the test image sequences on average. If images were classified as ‘uninformative’, which led to re-initialization of the algorithm, this was predictive for the result of dark region segmentation accuracy.
In conclusion, the developed target detection algorithm provided accurate results and is thought to provide reliable assistance in the clinic. The clinical relevance of this kind of navigation and control is currently being investigated.
|Name||Lecture Notes in Computer Science|
|Publisher||Springer International Publishing|
|Workshop||First International Workshop on Computer-Assisted and Robotic Endoscopy, CARE 2014|
|Period||14/09/14 → 18/09/14|
|Other||14-18 September 2014|
- robotized endoscopy
- image classification
- Flexible endoscopy
- Image-based navigation