4 research outputs found
AIMS: All-Inclusive Multi-Level Segmentation
Despite the progress of image segmentation for accurate visual entity
segmentation, completing the diverse requirements of image editing applications
for different-level region-of-interest selections remains unsolved. In this
paper, we propose a new task, All-Inclusive Multi-Level Segmentation (AIMS),
which segments visual regions into three levels: part, entity, and relation
(two entities with some semantic relationships). We also build a unified AIMS
model through multi-dataset multi-task training to address the two major
challenges of annotation inconsistency and task correlation. Specifically, we
propose task complementarity, association, and prompt mask encoder for
three-level predictions. Extensive experiments demonstrate the effectiveness
and generalization capacity of our method compared to other state-of-the-art
methods on a single dataset or the concurrent work on segmenting anything. We
will make our code and training model publicly available.Comment: Technical Repor
Gesture Recognition in Robotic Surgery: a Review
OBJECTIVE: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. METHODS: An article search was performed on 5 bibliographic databases with combinations of the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. RESULTS: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. CONCLUSION: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. SIGNIFICANCE: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field
Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit
Robotic-assisted surgery is now well-established in clinical practice and has
become the gold standard clinical treatment option for several clinical
indications. The field of robotic-assisted surgery is expected to grow
substantially in the next decade with a range of new robotic devices emerging
to address unmet clinical needs across different specialities. A vibrant
surgical robotics research community is pivotal for conceptualizing such new
systems as well as for developing and training the engineers and scientists to
translate them into practice. The da Vinci Research Kit (dVRK), an academic and
industry collaborative effort to re-purpose decommissioned da Vinci surgical
systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical
robotics research, has been a key initiative for addressing a barrier to entry
for new research groups in surgical robotics. In this paper, we present an
extensive review of the publications that have been facilitated by the dVRK
over the past decade. We classify research efforts into different categories
and outline some of the major challenges and needs for the robotics community
to maintain this initiative and build upon it