3 research outputs found
Online Disjoint Set Cover Without Prior Knowledge
The disjoint set cover (DSC) problem is a fundamental combinatorial optimization problem concerned with partitioning the (hyper)edges of a hypergraph into (pairwise disjoint) clusters so that the number of clusters that cover all nodes is maximized. In its online version, the edges arrive one-by-one and should be assigned to clusters in an irrevocable fashion without knowing the future edges. This paper investigates the competitiveness of online DSC algorithms. Specifically, we develop the first (randomized) online DSC algorithm that guarantees a poly-logarithmic (O(log^{2} n)) competitive ratio without prior knowledge of the hypergraph\u27s minimum degree. On the negative side, we prove that the competitive ratio of any randomized online DSC algorithm must be at least Omega((log n)/(log log n)) (even if the online algorithm does know the minimum degree in advance), thus establishing the first lower bound on the competitive ratio of randomized online DSC algorithms
Using Hand Pose Estimation To Automate Open Surgery Training Feedback
Purpose: This research aims to facilitate the use of state-of-the-art
computer vision algorithms for the automated training of surgeons and the
analysis of surgical footage. By estimating 2D hand poses, we model the
movement of the practitioner's hands, and their interaction with surgical
instruments, to study their potential benefit for surgical training.
Methods: We leverage pre-trained models on a publicly-available hands dataset
to create our own in-house dataset of 100 open surgery simulation videos with
2D hand poses. We also assess the ability of pose estimations to segment
surgical videos into gestures and tool-usage segments and compare them to
kinematic sensors and I3D features. Furthermore, we introduce 6 novel surgical
dexterity proxies stemming from domain experts' training advice, all of which
our framework can automatically detect given raw video footage.
Results: State-of-the-art gesture segmentation accuracy of 88.35\% on the
Open Surgery Simulation dataset is achieved with the fusion of 2D poses and I3D
features from multiple angles. The introduced surgical skill proxies presented
significant differences for novices compared to experts and produced actionable
feedback for improvement.
Conclusion: This research demonstrates the benefit of pose estimations for
open surgery by analyzing their effectiveness in gesture segmentation and skill
assessment. Gesture segmentation using pose estimations achieved comparable
results to physical sensors while being remote and markerless. Surgical
dexterity proxies that rely on pose estimation proved they can be used to work
towards automated training feedback. We hope our findings encourage additional
collaboration on novel skill proxies to make surgical training more efficient.Comment: Accepted to IPCAI 2023, 12 pages, 5 figure
Kinematic Data-Based Action Segmentation for Surgical Applications
Action segmentation is a challenging task in high-level process analysis,
typically performed on video or kinematic data obtained from various sensors.
In the context of surgical procedures, action segmentation is critical for
workflow analysis algorithms. This work presents two contributions related to
action segmentation on kinematic data. Firstly, we introduce two multi-stage
architectures, MS-TCN-BiLSTM and MS-TCN-BiGRU, specifically designed for
kinematic data. The architectures consist of a prediction generator with
intra-stage regularization and Bidirectional LSTM or GRU-based refinement
stages. Secondly, we propose two new data augmentation techniques, World Frame
Rotation and Horizontal-Flip, which utilize the strong geometric structure of
kinematic data to improve algorithm performance and robustness. We evaluate our
models on three datasets of surgical suturing tasks: the Variable Tissue
Simulation (VTS) Dataset and the newly introduced Bowel Repair Simulation (BRS)
Dataset, both of which are open surgery simulation datasets collected by us, as
well as the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a
well-known benchmark in robotic surgery. Our methods achieve state-of-the-art
performance on all benchmark datasets and establish a strong baseline for the
BRS dataset.Comment: 15 pages, 8 figure