Attachment Anchors: A Novel Framework for Laparoscopic Grasping Point Prediction in Colorectal Surgery

TL;DR

This paper introduces attachment anchors, a structured representation for predicting grasping points in colorectal surgery, improving accuracy especially in unseen scenarios, and facilitating autonomous tissue manipulation.

Contribution

The paper proposes attachment anchors as a novel structured representation that encodes tissue and attachment relationships, enhancing grasping point prediction in colorectal surgery.

Findings

Attachment anchors improve grasping prediction accuracy.

Strong performance gains in out-of-distribution scenarios.

Attachment anchors are predicted from laparoscopic images.

Abstract

Accurate grasping point prediction is a key challenge for autonomous tissue manipulation in minimally invasive surgery, particularly in complex and variable procedures such as colorectal interventions. Due to their complexity and prolonged duration, colorectal procedures have been underrepresented in current research. At the same time, they pose a particularly interesting learning environment due to repetitive tissue manipulation, making them a promising entry point for autonomous, machine learning-driven support. Therefore, in this work, we introduce attachment anchors, a structured representation that encodes the local geometric and mechanical relationships between tissue and its anatomical attachments in colorectal surgery. This representation reduces uncertainty in grasping point prediction by normalizing surgical scenes into a consistent local reference frame. We demonstrate that attachment anchors can be predicted from laparoscopic images and incorporated into a grasping framework based on machine learning. Experiments on a dataset of 90 colorectal surgeries demonstrate that attachment anchors improve grasping point prediction compared to image-only baselines. There are particularly strong gains in out-of-distribution settings, including unseen procedures and operating surgeons. These results suggest that attachment anchors are an effective intermediate representation for learning-based tissue manipulation in colorectal surgery.