56 research outputs found
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Automatic surgical phase recognition is a challenging and crucial task with
the potential to improve patient safety and become an integral part of
intra-operative decision-support systems. In this paper, we propose, for the
first time in workflow analysis, a Multi-Stage Temporal Convolutional Network
(MS-TCN) that performs hierarchical prediction refinement for surgical phase
recognition. Causal, dilated convolutions allow for a large receptive field and
online inference with smooth predictions even during ambiguous transitions. Our
method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy
videos with and without the use of additional surgical tool information.
Outperforming various state-of-the-art LSTM approaches, we verify the
suitability of the proposed causal MS-TCN for surgical phase recognition.Comment: 10 pages, 2 figure
Basics and principles of particle image velocimetry (PIV) for mapping biogenic and biologically relevant flows
Facile and cost-effective production of microscale PDMS architectures using a combined micromilling-replica moulding (μMi-REM) technique
Improvement of Particle Alignment Control and Precise Image Acquisition for On-Chip High-Speed Imaging Cell Sorter
Micro PIV measurements in micro cell sorters and mixing structures with three-dimensional flow behavior
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