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

October 21st, 2016 Irfan Essa Posted in Aneeq Zia, Awards, Computer Vision, Daniel Castro, Medical, MICCAI No Comments »

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.

 

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Paper in IJCARS (2016) on “Automated video-based assessment of surgical skills for training and evaluation in medical schools”

September 2nd, 2016 Irfan Essa Posted in Activity Recognition, Aneeq Zia, Computer Vision, Eric Sarin, Mark Clements, Medical, MICCAI, Thomas Ploetz, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • A. Zia, Y. Sharma, V. Bettadapura, E. L. Sarin, T. Ploetz, M. A. Clements, and I. Essa (2016), “Automated video-based assessment of surgical skills for training and evaluation in medical schools,” International Journal of Computer Assisted Radiology and Surgery, vol. 11, iss. 9, pp. 1623-1636, 2016. [WEBSITE] [DOI] [BIBTEX]
    @Article{    2016-Zia-AVASSTEMS,
      author  = {Zia, Aneeq and Sharma, Yachna and Bettadapura,
          Vinay and Sarin, Eric L and Ploetz, Thomas and
          Clements, Mark A and Essa, Irfan},
      doi    = {10.1007/s11548-016-1468-2},
      journal  = {International Journal of Computer Assisted
          Radiology and Surgery},
      month    = {September},
      number  = {9},
      pages    = {1623--1636},
      publisher  = {Springer Berlin Heidelberg},
      title    = {Automated video-based assessment of surgical skills
          for training and evaluation in medical schools},
      url    = {http://link.springer.com/article/10.1007/s11548-016-1468-2},
      volume  = {11},
      year    = {2016}
    }

Abstract

2016-Zia-AVASSTEMS

Sample frames from our video dataset

Purpose: Routine evaluation of basic surgical skills in medical schools requires considerable time and effort from supervising faculty. For each surgical trainee, a supervisor has to observe the trainees in- person. Alternatively, supervisors may use training videos, which reduces some of the logistical overhead. All these approaches, however, are still incredibly time consuming and involve human bias. In this paper, we present an automated system for surgical skills assessment by analyzing video data of surgical activities.

Method : We compare different techniques for video-based surgical skill evaluation. We use techniques that capture the motion information at a coarser granularity using symbols or words, extract motion dynamics using textural patterns in a frame kernel matrix, and analyze fine-grained motion information using frequency analysis. Results: We were successfully able to classify surgeons into different skill levels with high accuracy. Our results indicate that fine-grained analysis of motion dynamics via frequency analysis is most effective in capturing the skill relevant information in surgical videos.

Conclusion: Our evaluations show that frequency features perform better than motion texture features, which in turn perform better than symbol/word-based features. Put succinctly, skill classification accuracy is positively correlated with motion granularity as demonstrated by our results on two challenging video datasets.

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Paper in MICCAI (2015): “Automated Assessment of Surgical Skills Using Frequency Analysis”

October 6th, 2015 Irfan Essa Posted in Activity Recognition, Aneeq Zia, Eric Sarin, Mark Clements, Medical, MICCAI, Papers, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • A. Zia, Y. Sharma, V. Bettadapura, E. Sarin, M. Clements, and I. Essa (2015), “Automated Assessment of Surgical Skills Using Frequency Analysis,” in International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), 2015. [PDF] [BIBTEX]
    @InProceedings{    2015-Zia-AASSUFA,
      author  = {A. Zia and Y. Sharma and V. Bettadapura and E.
          Sarin and M. Clements and I. Essa},
      booktitle  = {International Conference on Medical Image Computing
          and Computer Assisted Interventions (MICCAI)},
      month    = {October},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2015-Zia-AASSUFA.pdf},
      title    = {Automated Assessment of Surgical Skills Using
          Frequency Analysis},
      year    = {2015}
    }

Abstract

We present an automated framework for a visual assessment of the expertise level of surgeons using the OSATS (Objective Structured Assessment of Technical Skills) criteria. Video analysis technique for extracting motion quality via  frequency coefficients is introduced. The framework is tested in a case study that involved analysis of videos of medical students with different expertise levels performing basic surgical tasks in a surgical training lab setting. We demonstrate that transforming the sequential time data into frequency components effectively extracts the useful information differentiating between different skill levels of the surgeons. The results show significant performance improvements using DFT and DCT coefficients over known state-of-the-art techniques.

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