From Monocular Vision to Autonomous Action: Guiding Tumor Resection via 3D Reconstruction

no code implementations • 20 Mar 2025 • Ayberk Acar, Mariana Smith, Lidia Al-Zogbi, Tanner Watts, Fangjie Li, Hao Li, Nural Yilmaz, Paul Maria Scheikl, Jesse F. d'Almeida, Susheela Sharma, Lauren Branscombe, Tayfun Efe Ertop, Robert J. Webster III, Ipek Oguz, Alan Kuntz, Axel Krieger, Jie Ying Wu

Surgical automation requires precise guidance and understanding of the scene.

3D Reconstruction Anatomy +1

An Effectiveness Study Across Baseline and Learning-based Force Estimation Methods on the da Vinci Research Kit Si System

no code implementations • 13 May 2024 • Hao Yang, Ayberk Acar, Keshuai Xu, Anton Deguet, Peter Kazanzides, Jie Ying Wu

Our prior work restored force sensing through machine learning-based force estimation for the da Vinci Research Kit (dVRK) Classic.

MeshBrush: Painting the Anatomical Mesh with Neural Stylization for Endoscopy

1 code implementation • 3 Apr 2024 • John J. Han, Ayberk Acar, Nicholas Kavoussi, Jie Ying Wu

We demonstrate that mesh stylization is a promising approach for creating realistic simulations for downstream tasks such as training networks and preoperative planning.

Neural Stylization Style Transfer +1

Depth Anything in Medical Images: A Comparative Study

no code implementations • 29 Jan 2024 • John J. Han, Ayberk Acar, Callahan Henry, Jie Ying Wu

Monocular depth estimation (MDE) is a critical component of many medical tracking and mapping algorithms, particularly from endoscopic or laparoscopic video.

Monocular Depth Estimation Self-Supervised Learning