MT3DNet: Multi-Task learning Network for 3D Surgical Scene Reconstruction

TL;DR

This paper introduces MT3DNet, a multi-task learning network that simultaneously performs 3D scene reconstruction, segmentation, depth estimation, and instrument detection in surgical images, improving real-time understanding of surgical scenes.

Contribution

The paper presents a novel multi-task learning framework with adversarial weight updates that effectively integrates 3D reconstruction with segmentation and detection tasks in surgical scenes.

Findings

Achieves accurate 3D reconstruction, segmentation, and detection on EndoVis2018 dataset.

Outperforms existing methods lacking 3D capabilities in surgical scene understanding.

Demonstrates efficient multi-task learning with improved optimization techniques.

Abstract

In image-assisted minimally invasive surgeries (MIS), understanding surgical scenes is vital for real-time feedback to surgeons, skill evaluation, and improving outcomes through collaborative human-robot procedures. Within this context, the challenge lies in accurately detecting, segmenting, and estimating the depth of surgical scenes depicted in high-resolution images, while simultaneously reconstructing the scene in 3D and providing segmentation of surgical instruments along with detection labels for each instrument. To address this challenge, a novel Multi-Task Learning (MTL) network is proposed for performing these tasks concurrently. A key aspect of this approach involves overcoming the optimization hurdles associated with handling multiple tasks concurrently by integrating a Adversarial Weight Update into the MTL framework, the proposed MTL model achieves 3D reconstruction through the integration of segmentation, depth estimation, and object detection, thereby enhancing the understanding of surgical scenes, which marks a significant advancement compared to existing studies that lack 3D capabilities. Comprehensive experiments on the EndoVis2018 benchmark dataset underscore the adeptness of the model in efficiently addressing all three tasks, demonstrating the efficacy of the proposed techniques.