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Point detection through multi-instance deep heatmap regression for sutures in endoscopy.

Lalith Sharan¹, Gabriele Romano², Julian Brand³

¹Department of Internal Medicine III, Group Artificial Intelligence in Cardiovascular Medicine, Heidelberg University Hospital, 69120, Heidelberg, Germany. lalithnag.sharangururaj@med.uni-heidelberg.de.

International Journal of Computer Assisted Radiology and Surgery

|November 8, 2021

Summary

This summary is machine-generated.

This study introduces a novel deep heatmap regression model for detecting sutures in minimally invasive heart valve repair images. The new method improves suture localization accuracy in both real surgical and simulated environments.

Keywords:

Endoscopy Mitral valve repair Point detection

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Area of Science:

Medical Imaging
Computer Vision
Surgical Robotics

Background:

Minimally invasive mitral valve repair requires precise suture visualization for analysis and augmented reality.
Existing landmark detection methods are insufficient for variable suture presence and location in endoscopic images.

Purpose of the Study:

To develop an automated suture detection method for endoscopic cardiac surgery images.
To address the challenge of varying suture numbers and locations in the mitral valve annulus.

Main Methods:

Formulated suture detection as a multi-instance deep heatmap regression problem.
Introduced a novel approach using a 2D Gaussian layer and a differentiable 2D spatial Soft-Argmax layer for non-maximum suppression.

Main Results:

The proposed model demonstrated improved performance over the baseline in both intra-operative and simulator domains.
Variant 1 of the model showed significant mean improvements in accuracy metrics.

Conclusions:

The developed model enhances suture detection accuracy in minimally invasive cardiac surgery.
Publicly available data and code facilitate further research and development in this area.