Exploring Segment-level Semantics for Online Phase Recognition from Surgical Videos

TL;DR

This paper introduces a hierarchical segment-based approach for surgical phase recognition in videos, emphasizing the importance of segment-level semantics over frame-wise analysis to improve accuracy.

Contribution

The paper proposes a novel segment-attentive hierarchical network that extracts and utilizes high-level semantic segments to refine surgical phase predictions, addressing ambiguity in frames.

Findings

Outperforms previous state-of-the-art methods on public datasets.

Effectiveness of hierarchical segments and attention modules validated.

Code released for reproducibility and further research.

Abstract

Automatic surgical phase recognition plays a vital role in robot-assisted surgeries. Existing methods ignored a pivotal problem that surgical phases should be classified by learning segment-level semantics instead of solely relying on frame-wise information. This paper presents a segment-attentive hierarchical consistency network (SAHC) for surgical phase recognition from videos. The key idea is to extract hierarchical high-level semantic-consistent segments and use them to refine the erroneous predictions caused by ambiguous frames. To achieve it, we design a temporal hierarchical network to generate hierarchical high-level segments. Then, we introduce a hierarchical segment-frame attention module to capture relations between the low-level frames and high-level segments. By regularizing the predictions of frames and their corresponding segments via a consistency loss, the network can generate semantic-consistent segments and then rectify the misclassified predictions caused by ambiguous low-level frames. We validate SAHC on two public surgical video datasets, i.e., the M2CAI16 challenge dataset and the Cholec80 dataset. Experimental results show that our method outperforms previous state-of-the-arts and ablation studies prove the effectiveness of our proposed modules. Our code has been released at: https://github.com/xmed-lab/SAHC.