MT3DNet: Multi-Task learning Network for 3D Surgical Scene Reconstruction
This work addresses the need for real-time 3D surgical scene understanding in minimally invasive surgeries, which is incremental as it builds on existing multi-task learning methods by adding adversarial weight updates.
The paper tackled the problem of accurately detecting, segmenting, and estimating depth in surgical scenes from high-resolution images to enable 3D reconstruction, achieving state-of-the-art results on the EndoVis2018 benchmark dataset.
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.