Image Compositing for Segmentation of Surgical Tools without Manual Annotations

TL;DR

This paper introduces a semi-synthetic data generation method for training surgical instrument segmentation models without manual annotations, leveraging chroma key techniques and novel augmentation to achieve comparable performance to manually labeled datasets.

Contribution

The authors propose a semi-synthetic dataset creation approach using chroma key and innovative blending methods, eliminating the need for manual pixel-wise annotations in surgical tool segmentation.

Findings

Semi-synthetic data training matches real dataset performance.

Simple post-processing improves segmentation accuracy.

Method reduces manual labeling effort significantly.

Abstract

Producing manual, pixel-accurate, image segmentation labels is tedious and time-consuming. This is often a rate-limiting factor when large amounts of labeled images are required, such as for training deep convolutional networks for instrument-background segmentation in surgical scenes. No large datasets comparable to industry standards in the computer vision community are available for this task. To circumvent this problem, we propose to automate the creation of a realistic training dataset by exploiting techniques stemming from special effects and harnessing them to target training performance rather than visual appeal. Foreground data is captured by placing sample surgical instruments over a chroma key (a.k.a. green screen) in a controlled environment, thereby making extraction of the relevant image segment straightforward. Multiple lighting conditions and viewpoints can be captured and introduced in the simulation by moving the instruments and camera and modulating the light source. Background data is captured by collecting videos that do not contain instruments. In the absence of pre-existing instrument-free background videos, minimal labeling effort is required, just to select frames that do not contain surgical instruments from videos of surgical interventions freely available online. We compare different methods to blend instruments over tissue and propose a novel data augmentation approach that takes advantage of the plurality of options. We show that by training a vanilla U-Net on semi-synthetic data only and applying a simple post-processing, we are able to match the results of the same network trained on a publicly available manually labeled real dataset.