Real-Time Segmentation of Non-Rigid Surgical Tools based on Deep Learning and Tracking

TL;DR

This paper introduces a real-time surgical tool segmentation method combining deep neural networks and optical flow, achieving high accuracy and robustness in clinical scenarios with deformable tools.

Contribution

The paper presents a novel real-time segmentation approach that integrates FCNs with optical flow tracking, enabling accurate and fast segmentation of deformable surgical tools.

Findings

Achieves 89.6% balanced accuracy with deep learning alone.

Real-time method attains 78.2% accuracy across datasets.

Outperforms previous non-real-time methods by 3.8 percentage points.

Abstract

Real-time tool segmentation is an essential component in computer-assisted surgical systems. We propose a novel real-time automatic method based on Fully Convolutional Networks (FCN) and optical flow tracking. Our method exploits the ability of deep neural networks to produce accurate segmentations of highly deformable parts along with the high speed of optical flow. Furthermore, the pre-trained FCN can be fine-tuned on a small amount of medical images without the need to hand-craft features. We validated our method using existing and new benchmark datasets, covering both ex vivo and in vivo real clinical cases where different surgical instruments are employed. Two versions of the method are presented, non-real-time and real-time. The former, using only deep learning, achieves a balanced accuracy of 89.6% on a real clinical dataset, outperforming the (non-real-time) state of the art by 3.8% points. The latter, a combination of deep learning with optical flow tracking, yields an average balanced accuracy of 78.2% across all the validated datasets.