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Accurate instrument segmentation in endoscopic vision of robot-assistedsurgery is challenging due to reflection on the instruments and frequentcontacts with tissue. Deep neural networks (DNN) show competitive performanceand are in favor in recent years. However, the hunger of DNN for labeled dataposes a huge workload of annotation. Motivated by alleviating this workload, wepropose a general embeddable method to decrease the usage of labeled realimages, using active generated synthetic images. In each active learningiteration, the most informative unlabeled images are first queried by activelearning and then labeled. Next, synthetic images are generated based on theseselected images. The instruments and backgrounds are cropped out and randomlycombined with each other with blending and fusion near the boundary. Theeffectiveness of the proposed method is validated on 2 sinus surgery datasetsand 1 intraabdominal surgery dataset. The results indicate a considerableimprovement in performance, especially when the budget for annotation is small.The effectiveness of different types of synthetic images, blending methods, andexternal background are also studied. All the code is open-sourced at:https://github.com/HaonanPeng/active_syn_generator.

Reducing Annotating Load: Active Learning with Synthetic Images in Surgical Instrument Segmentation