53 citations · 64 across the 10 of their papers we have counts for
11 papers
Point Tracking in Surgery--The 2025 Surgical Tattoos in Infrared Challenge (STIRC2025)
Adam Schmidt, Mert Asim Karaoglu, Zijian Wu +32
Point tracking in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning…
Track2Map: Online Deformable SLAM with Motion-Aware Pose Optimization in Robotic Surgery
Tianyi Song, Sierra Bonilla, Xinwei Ju +6
Gaussian splatting is the current state-of-the-art for dense, deformable 3D anatomy reconstruction in robot-assisted minimally invasive surgery (RAMIS); however, most pipelines are…
Point Tracking in Surgery--The 2024 Surgical Tattoos in Infrared (STIR) Challenge
Adam Schmidt, Mert Asim Karaoglu, Soham Sinha +37
Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-b…
SurgPose: a Dataset for Articulated Robotic Surgical Tool Pose Estimation and Tracking
Zijian Wu, Adam Schmidt, Randy Moore +4
Accurate and efficient surgical robotic tool pose estimation is of fundamental significance to downstream applications such as augmented reality (AR) in surgical training and learn…
Image Retrieval with Intra-Sweep Representation Learning for Neck Ultrasound Scanning Guidance
Wanwen Chen, Adam Schmidt, Eitan Prisman +1
Purpose: Intraoperative ultrasound (US) can enhance real-time visualization in transoral robotic surgery. The surgeon creates a mental map with a pre-operative scan. Then, a surgic…
A-MFST: Adaptive Multi-Flow Sparse Tracker for Real-Time Tissue Tracking Under Occlusion
Yuxin Chen, Zijian Wu, Adam Schmidt +1
Purpose: Tissue tracking is critical for downstream tasks in robot-assisted surgery. The Sparse Efficient Neural Depth and Deformation (SENDD) model has previously demonstrated acc…
Augmenting Efficient Real-time Surgical Instrument Segmentation in Video with Point Tracking and Segment Anything
Zijian Wu, Adam Schmidt, Peter Kazanzides +1
The Segment Anything Model (SAM) is a powerful vision foundation model that is revolutionizing the traditional paradigm of segmentation. Despite this, a reliance on prompting each…
PIPsUS: Self-Supervised Point Tracking in Ultrasound
Wanwen Chen, Adam Schmidt, Eitan Prisman +1
Finding point-level correspondences is a fundamental problem in ultrasound (US), since it can enable US landmark tracking for intraoperative image guidance in different surgeries,…
Tracking and Mapping in Medical Computer Vision: A Review
Adam Schmidt, Omid Mohareri, Simon DiMaio +2
As computer vision algorithms increase in capability, their applications in clinical systems will become more pervasive. These applications include: diagnostics, such as colonoscop…
Surgical Tattoos in Infrared: A Dataset for Quantifying Tissue Tracking and Mapping
Adam Schmidt, Omid Mohareri, Simon DiMaio +1
Quantifying performance of methods for tracking and mapping tissue in endoscopic environments is essential for enabling image guidance and automation of medical interventions and s…
SENDD: Sparse Efficient Neural Depth and Deformation for Tissue Tracking
Adam Schmidt, Omid Mohareri, Simon DiMaio +1
Deformable tracking and real-time estimation of 3D tissue motion is essential to enable automation and image guidance applications in robotically assisted surgery. Our model, Spars…