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Understanding tissue motion in surgery is crucial to enable applications in downstream tasks such as segmentation, 3D reconstruction, virtual tissue landmarking, autonomous probe-based scanning, and subtask autonomy. Labeled data are…
Accurate tracking of tissues and instruments in videos is crucial for Robotic-Assisted Minimally Invasive Surgery (RAMIS), as it enables the robot to comprehend the surgical scene with precise locations and interactions of tissues and…
Imaging mass spectrometry (IMS) is a powerful tool for untargeted, highly multiplexed molecular mapping of tissue in biomedical research. IMS offers a means of mapping the spatial distributions of molecular species in biological tissue with…
Mid-InfraRed spectroscopy is a promising label-free technique that can offer insights into morphological and pathological alterations in biological tissues at the molecular level. Owing to the development of the Fourier Transform InfraRed…
Thermal infrared imaging exhibits considerable potentials for robotic perception tasks, especially in environments with poor visibility or challenging lighting conditions. However, TIR images typically suffer from heavy non-uniform…
The usage of both off-the-shelf and end-to-end trained deep networks have significantly improved performance of visual tracking on RGB videos. However, the lack of large labeled datasets hampers the usage of convolutional neural networks…
Synthetic datasets have enabled significant progress in point tracking by providing large-scale, densely annotated supervision. However, deploying these models in real-world domains remains challenging due to domain shift and lack of…
Thermal infrared (TIR) pedestrian tracking is one of the important components among numerous applications of computer vision, which has a major advantage: it can track pedestrians in total darkness. The ability to evaluate the TIR…
Validation of newly developed optical tissue sensing techniques for tumor detection during cancer surgery requires an accurate correlation with histological results. Additionally, such accurate correlation facilitates precise data labeling…
Thermal infrared (TIR) target tracking methods often adopt the correlation filter (CF) framework due to its computational efficiency. However, the low resolution of TIR images, along with tracking interference, significantly limits the…
This paper tackles instrument tracking and 3D visualization challenges in minimally invasive surgery (MIS), crucial for computer-assisted interventions. Conventional and robot-assisted MIS encounter issues with limited 2D camera projections…
In this paper, we present a Large-Scale and high-diversity general Thermal InfraRed (TIR) Object Tracking Benchmark, called LSOTBTIR, which consists of an evaluation dataset and a training dataset with a total of 1,400 TIR sequences and…
Augmented Reality (AR) surgical navigation systems are emerging as the next generation of intraoperative surgical guidance, promising to overcome limitations of traditional navigation systems. However, known issues with AR depth perception…
Minimally invasive surgery presents challenges such as dynamic tissue motion and a limited field of view. Accurate tissue tracking has the potential to support surgical guidance, improve safety by helping avoid damage to sensitive…
The mainstream CNN-based remote sensing (RS) image semantic segmentation approaches typically rely on massive labeled training data. Such a paradigm struggles with the problem of RS multi-view scene segmentation with limited labeled views…
Fourier-transform infrared spectroscopy (FTIR) is a powerful analytical method for not only the chemical identification of solid, liquid, and gas species, but also the quantification of their concentration. However, the chemical…
This paper presents a novel mid-wave infrared (MWIR) small target detection dataset (MWIRSTD) comprising 14 video sequences containing approximately 1053 images with annotated targets of three distinct classes of small objects. Captured…
We present STaR, a novel method that performs Self-supervised Tracking and Reconstruction of dynamic scenes with rigid motion from multi-view RGB videos without any manual annotation. Recent work has shown that neural networks are…
Tool tracking in surgical videos is essential for advancing computer-assisted interventions, such as skill assessment, safety zone estimation, and human-machine collaboration. However, the lack of context-rich datasets limits AI…
This study investigates the noise characteristics of intraoperative X-ray fluoroscopic images acquired during real-time image-guided radiotherapy (IGRT), and presents a novel noise image generation method based on the identified noise…