Rethinking Anticipation Tasks: Uncertainty-aware Anticipation of Sparse Surgical Instrument Usage for Context-aware Assistance

TL;DR

This paper introduces a novel probabilistic approach for anticipating surgical instrument usage in laparoscopic videos, effectively handling sparse annotations and uncertainty, thereby advancing context-aware surgical assistance.

Contribution

It presents the first method for instrument anticipation in surgery that requires only sparse annotations and models uncertainty without dense segmentation.

Findings

Outperforms baseline methods in anticipation accuracy

Effective in quantifying uncertainties related to future instrument usage

Competitive with methods using richer annotations

Abstract

Intra-operative anticipation of instrument usage is a necessary component for context-aware assistance in surgery, e.g. for instrument preparation or semi-automation of robotic tasks. However, the sparsity of instrument occurrences in long videos poses a challenge. Current approaches are limited as they assume knowledge on the timing of future actions or require dense temporal segmentations during training and inference. We propose a novel learning task for anticipation of instrument usage in laparoscopic videos that overcomes these limitations. During training, only sparse instrument annotations are required and inference is done solely on image data. We train a probabilistic model to address the uncertainty associated with future events. Our approach outperforms several baselines and is competitive to a variant using richer annotations. We demonstrate the model's ability to quantify task-relevant uncertainties. To the best of our knowledge, we are the first to propose a method for anticipating instruments in surgery.