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

Dennis N. Schneider, Lars Wagner, Daniel Rueckert, Dirk Wilhelm

TL;DR

This work introduces attachment anchors, a structured representation that encodes the local geometric and mechanical relationships between tissue and its anatomical attachments in colorectal surgery that reduces uncertainty in grasping point prediction by normalizing surgical scenes into a consistent local reference frame.

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.

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

TL;DR

This work introduces attachment anchors, a structured representation that encodes the local geometric and mechanical relationships between tissue and its anatomical attachments in colorectal surgery that reduces uncertainty in grasping point prediction by normalizing surgical scenes into a consistent local reference frame.

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.
Paper Structure (19 sections, 5 equations, 7 figures, 7 tables)

This paper contains 19 sections, 5 equations, 7 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Analysis
  4. Task Formulation
  5. Case Discrimination
  6. Methodology
  7. Attachment Anchor Encoder
  8. Grasping Point Decoder
  9. Dataset
  10. Evaluation Metrics
  11. Experiments and Results
  12. Statistical Analysis
  13. Attachment Anchor Prediction
  14. Experiments Using Attachment Anchors
  15. Generalization to Unseen Surgeries
  16. ...and 4 more sections

Figures (7)

  • Figure 1: Pipeline of the proposed grasping point prediction framework. The model first detects and classifies the attachment anchor representation from the input image and then predicts a grasping point relative to the anchor origin.
  • Figure 2: Example surgical input images illustrating the three attachment anchor cases. Each image shows the dissection point $D$ (top) and the corresponding attachment anchor representation (bottom), with the soft tissue of interest highlighted in blue.
  • Figure 3: Architecture of the proposed model. The attachment anchor encoder $\phi_A$ (blue) extracts the anchor representation, which is then used by the grasping point decoder $\phi_G$ (cyan).
  • Figure 4: Dataset composition showing colorectal surgical procedures stratified by anatomical region along the colon.
  • Figure 5: Statistical distribution of grasping points before and after normalizing with attachment anchors, per case.
  • ...and 2 more figures