Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

Automatic visual detection of instruments in minimally invasive surgery (MIS) can significantly augment the procedure experience for operating clinicians. In this paper, we present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector. While such detectors are typically slow to use, we introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time, ultimately allowing it to run at framerate. We evaluate the effectiveness of our approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed.


Editor(s):
Golland, P
Hata, N
Barillot, C
Hornegger, J
Howe, R
Presented at:
International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, Massachusetts, USA, September 14-18, 2014
Year:
2014
Publisher:
Berlin, Springer
ISBN:
978-3-319-10469-0
Keywords:
Laboratories:




 Record created 2014-05-01, last modified 2018-09-13

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