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 https://github.com/wrld/Ada-Tracker.

Untreated pain in critically ill patients can lead to immunosuppression and increased metabolic activity, with severe clinical consequences such as tachypnea and delirium. Continuous pain assessment is challenging due to nursing shortages and intensive care unit (ICU) workload. Mechanical ventilation equipment obscures the facial features of many patients in the ICU, making previous facial pain detection methods based on full-face images inapplicable. This paper proposes a facial Action Units (AUs) guided pain assessment network for faces under occlusion. The network consists of an AU-guided (AUG) module, a texture feature extraction (TFE) module, and a pain assessment (PA) module. The AUG module automatically detects AUs in the non-occluded areas of the face. In contrast, the TFE module detects the facial landmarks and crops prior knowledge patches, a random exploration patch, and a global feature patch. Then these patches are fed into two convolutional networks to extract texture features. Afterward, the designed AU guidances and texture features are fused in the PA module to assess the pain state. Extensive validation is conducted on a public dataset and two datasets created in this work. The proposed network architecture achieves superior performance in binary classification, four-class classification, and intensity regression tasks. In addition, we have successfully applied the network to actual data collected in the laboratory environment with excellent results.