PEg TRAnsfer Workflow recognition challenge report: Do multimodal data improve recognition? - Huaulmé, Arnaud; Harada, Kanako; Nguyen, Quang-Minh; Park, Bogyu; Hong, Seungbum; Choi, Min-Kook; Peven, Michael; Li, Yunshuang; Long, Yonghao; Dou, Qi; Kumar, Satyadwyoom; Lalithkumar, Seenivasan; et al. (Computer Methods and Programs in Biomedicine, 2023)
•Peg Transfer data set containing video, kinematic, semantic segmentation and workflow annotation.•Challenge of surgical workflow recognition with different modality.•Comparison of multiple deep learning based recognition methods. Background and objective: In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the “PEg TRAnsfer Workflow recognition” (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value. Methods: The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score. Results: Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks). Conclusion: The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.