Paper in M2CAI (MICCAI workshop) on “Fine-tuning Deep Architectures for Surgical Tool Detection and results of Tool Detection Challange”

Paper

  • A. Zia, D. Castro, and I. Essa (2016), “Fine-tuning Deep Architectures for Surgical Tool Detection,” in Workshop and Challenges on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), Held in Conjunction with International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, 2016. [PDF] [WEBSITE] [BIBTEX]
    @InProceedings{ 2016-Zia-FDASTD,
    address  = {Athens, Greece},
    author  = {Aneeq Zia and Daniel Castro and Irfan Essa},
    booktitle  = {{Workshop and Challenges on Modeling and Monitoring
    of Computer Assisted Interventions (M2CAI), Held in
    Conjunction with International Conference on Medical
    Image Computing and Computer Assisted Intervention
    (MICCAI)}},
    month = {October},
    pdf = {http://www.cc.gatech.edu/~irfan/p/2016-Zia-FDASTD.pdf},
    title = {Fine-tuning Deep Architectures for Surgical Tool
    Detection},
    url = {http://www.cc.gatech.edu/cpl/projects/deepm2cai/},
    year = {2016}
    }

Abstract

Visualization of some of the training videos.


Understanding surgical workflow has been a key concern of the medical research community. One of the main advantages of surgical workflow detection is real-time operating room (OR) scheduling. For hospitals, each minute of OR time is important in order to reduce cost and increase patient throughput. Traditional approaches in this field generally tackle the video analysis using hand-crafted video features to facilitate the tool detection. Recently, Twinanda et al. presented a CNN architecture ’EndoNet’ which outperformed previous methods for both surgical tool detection and surgical phase detection. Given the recent success of these networks, we present a study of various architectures coupled with a submission to the M2CAI Surgical Tool Detection challenge. We achieved a top-3 result for the M2CAI competition with a mAP of 37.6.

img_20161021_175424
Winners of 3rd place in the Challange

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.