Soft tissue tracking is crucial for computer-assisted interventions. Existing
approaches mainly rely on extracting discriminative features from the template
and videos to recover corresponding matches. However, it is difficult to adopt
these techniques in surgical scenes, where tissues are changing in shape and
appearance throughout the surgery. To address this problem, we exploit optical
flow to naturally capture the pixel-wise tissue deformations and adaptively
correct the tracked template. Specifically, we first implement an inter-frame
matching mechanism to extract a coarse region of interest based on optical flow
from consecutive frames. To accommodate appearance change and alleviate drift,
we then propose an adaptive-template matching method, which updates the tracked
template based on the reliability of the estimates. Our approach, Ada-Tracker,
enjoys both short-term dynamics modeling by capturing local deformations and
long-term dynamics modeling by introducing global temporal compensation. We
evaluate our approach on the public SurgT benchmark, which is generated from
Hamlyn, SCARED, and Kidney boundary datasets. The experimental results show
that Ada-Tracker achieves superior accuracy and performs more robustly against
prior works. Code is available at this https URL

通过光流技术自然捕捉像素级组织变形并自适应地校正被追踪模板，Ada-Tracker 方法采用了短期动态建模和长期动态建模，实现了在手术场景中更准确且更稳健的软组织跟踪。