  Accurate instrument segmentation in endoscopic vision of robot-assisted
surgery is challenging due to reflection on the instruments and frequent
contacts with tissue. Deep neural networks (DNN) show competitive performance
and are in favor in recent years. However, the hunger of DNN for labeled data
poses a huge workload of annotation. Motivated by alleviating this workload, we
propose a general embeddable method to decrease the usage of labeled real
images, using active generated synthetic images. In each active learning
iteration, the most informative unlabeled images are first queried by active
learning and then labeled. Next, synthetic images are generated based on these
selected images. The instruments and backgrounds are cropped out and randomly
combined with each other with blending and fusion near the boundary. The
effectiveness of the proposed method is validated on 2 sinus surgery datasets
and 1 intraabdominal surgery dataset. The results indicate a considerable
improvement in performance, especially when the budget for annotation is small.
The effectiveness of different types of synthetic images, blending methods, and
external background are also studied. All the code is open-sourced at:
https://github.com/HaonanPeng/active_syn_generator.
