Jianmin Yang

@conference{nokey,

title = {Frame Selection Methods to Streamline Surgical Video Annotation for Tool Detection Tasks},

author = {Jianming Yang and Rebecca Hisey and Joshua Bierbrier and Gabor Fichtinger and Christine Law and Matthew Holden},

publisher = {IEEE},

abstract = {Given the growing volume of surgical data and the

increasing demand for annotation, there is a pressing need to

streamline the annotation process for surgical videos. Previously,

annotation tools for object detection tasks have greatly evolved,

reducing time expense and enhancing ease. There are also many

initial frame selection approaches for Artificial Intelligence

(AI) assisted annotation tasks to further reduce human effort.

However, these methods have rarely been implemented and

reported in the context of surgical datasets, especially in cataract

surgery datasets. The identification of initial frames to annotate

before the use of any tools or algorithms determines annotation

efficiency. Therefore, in this paper, we chose to prioritize the

development of a method for selecting initial frames to facilitate

the subsequent automated annotation process. We propose a

customized initial frames selection method based on feature

clustering and compare it to commonly used temporal selection

methods. In each method, initial frames from cataract surgery

videos are selected to train a surgical tool detection model.

The model assists in the automated annotation process by

predicting bounding boxes for the surgery video objects in the

remaining frames. Evaluations of these methods are based on

how many edits users need to perform when annotating the

initial frames and how many edits users are expected to perform

to correct all predictions. Additionally, the total annotation cost

for each method is compared. Results indicate that on average,

the proposed cluster-based approach requires the fewest total

edits and exhibits the lowest total annotation cost compared

to conventional methods. These findings highlight a promising

direction for developing a complete application, featuring

streamlined AI-assisted annotation processes for surgical tool

detection tasks.},

keywords = {},

pubstate = {forthcoming},

tppubtype = {conference}

}