MIcro-Surgical Anastomose Workflow recognition challenge report

TL;DR

This paper reports on the MISAW challenge, which provided a dataset and tasks for recognizing micro-surgical workflows at multiple levels using deep learning, achieving high accuracy for phases and steps but lower for activities.

Contribution

It introduces a new publicly available dataset and benchmark for micro-surgical workflow recognition at multiple granularity levels using deep learning models.

Findings

High accuracy (>95%) for phase recognition

Moderate accuracy (80%) for step recognition

Lower accuracy (60%) for activity recognition

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