MIcro-Surgical Anastomose Workflow recognition challenge report

• URL: http://arxiv.org/abs/2103.13111v1
• Date: Wed, 24 Mar 2021 11:34:09 GMT
• Title: MIcro-Surgical Anastomose Workflow recognition challenge report
• Authors: Arnaud Huaulm\'e, Duygu Sarikaya, K\'evin Le Mut, Fabien Despinoy, Yonghao Long, Qi Dou, Chin-Boon Chng, Wenjun Lin, Satoshi Kondo, Laura Bravo-S\'anchez, Pablo Arbel\'aez, Wolfgang Reiter, Manoru Mitsuishi, Kanako Harada, Pierre Jannin
• Abstract summary: "MIcro-Surgical Anastomose recognition on training sessions" (MISAW) challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations described at three different granularity levels: phase, step, and activity. The best models achieved more than 95% AD-Accuracy for phase recognition, 80% for step recognition, 60% for activity recognition, and 75% for all granularity levels.
• Score: 12.252332806968756
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The "MIcro-Surgical Anastomose Workflow recognition on training sessions" (MISAW) challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations described at three different granularity levels: phase, step, and activity. The participants were given the option to use kinematic data and videos to develop workflow recognition models. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. One ranking was made for each task. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score. Six teams, including a non-competing team, participated in at least one task. All models employed deep learning models, such as CNN or RNN. The best models achieved more than 95% AD-Accuracy for phase recognition, 80% for step recognition, 60% for activity recognition, and 75% for all granularity levels. For high levels of granularity (i.e., phases and steps), the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time or resource management. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available to encourage further research in surgical workflow recognition. It can be found at www.synapse.org/MISAW

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