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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Paper in M2CAI 2014: “Video Based Assessment of OSATS Using Sequential Motion Textures”

September 14th, 2014 Irfan Essa Posted in Activity Recognition, Behavioral Imaging, Computer Vision, Medical, MICCAI, Papers, Thomas Ploetz, Vinay Bettadapura, Yachna Sharma No Comments »

Paper

  • Y. Sharma, V. Bettadapura, T. Ploetz, N. Hammerla, S. Mellor, R. McNaney, P. Olivier, S. Deshmukh, A. Mccaskie, and I. Essa (2014), “Video Based Assessment of OSATS Using Sequential Motion Textures,” in Proceedings of Workshop on Modeling and Monitoring of Computer Assisted Interventions (M2CAI), 2014. (Best Paper Honorable Mention Award) [PDF] [BIBTEX]
    @InProceedings{    2014-Sharma-VBAOUSMT,
      author  = {Yachna Sharma and Vinay Bettadapura and Thomas
          Ploetz and Nils Hammerla and Sebastian Mellor and
          Roisin McNaney and Patrick Olivier and Sandeep
          Deshmukh and Andrew Mccaskie and Irfan Essa},
      awards  = {(Best Paper Honorable Mention Award)},
      booktitle  = {{Proceedings of Workshop on Modeling and Monitoring
          of Computer Assisted Interventions (M2CAI)}},
      month    = {September},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2014-Sharma-VBAOUSMT.pdf}
          ,
      title    = {Video Based Assessment of OSATS Using Sequential
          Motion Textures},
      year    = {2014}
    }

Abstract

2014-Sharma-VBAOUSMTA fully automated framework for video-based surgical skill assessment is presented that incorporates the sequential and qualitative aspects of surgical motion in a data-driven manner. The Objective Structured Assessment of Technical Skills (OSATS) assessments is replicated, which provides both an overall and in-detail evaluation of basic suturing skills required for surgeons. Video analysis techniques are introduced that incorporate sequential motion aspects into motion textures. Significant performance improvement over standard bag-of-words and motion analysis approaches is demonstrated. The framework is evaluated in a case study that involved medical students with varying levels of expertise performing basic surgical tasks in a surgical training lab setting.

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PhD Thesis (2014) by Yachna Sharma “Surgical Skill Assessment Using Motion Texture analysis”

May 2nd, 2014 Irfan Essa Posted in Medical, PhD, Yachna Sharma No Comments »

Thesis title: Surgical Skill Assessment Using Motion Texture analysis

Yachna Sharma, Ph. D. Candidate, ECE
http://users.ece.gatech.edu/~ysharma3/

Committee:

Prof. Irfan Essa (advisor), College of Computing
Prof. Mark A. Clements (co-advisor), School of Electrical and Computer Engineering
Prof. David Anderson, School of Electrical and Computer Engineering
Prof. Anthony Yezzi, School of Electrical and Computer Engineering
Prof. Christopher F. Barnes, School of Electrical and Computer Engineering
Dr. Thomas Ploetz, Culture lab, School of Computing Science, Newcastle University, United Kingdom
Dr. Eric L. Sarin, Division of Cardiothoracic Surgery, Department of Surgery, Emory University School of Medicine

Abstract:

The objective of this Ph.D. research is to design and develop a framework for automated assessment of surgical skills.Automated assessment can help expedite the manual assessment process and provide unbiased evaluations with possible dexterity feedback.

Evaluation of surgical skills is an important aspect in training of medical students. Current practices rely on manual evaluations from faculty and residents and are time consuming. Proposed solutions in literature involve retrospective evaluations such as watching the offline videos. It requires precious time and attention of expert surgeons and may vary from one surgeon to another. With recent advancements in computer vision and machine learning techniques, the retrospective video evaluation can be best delegated to the computer algorithms.

Skill assessment is a challenging task requiring expert domain knowledge that may be difficult to translate into algorithms. To emulate this human observation process, an appropriate data collection mechanism is required to track motion of the surgeon’s hand in an unrestricted manner. In addition, it is essential to identify skill defining motion dynamics and skill relevant hand locations.

This Ph.D. research aims to address the limitations of manual skill assessment by developing an automated motion analysis framework. Specifically, we propose (1) to design and implement quantitative features to capture fine motion details from surgical video data, (2) to identify and test the efficacy of a core subset of features in classifying the surgical students into different expertise levels, (3) to derive absolute skill scores using regression methods and (4) to perform dexterity analysis using motion data from different hand locations.

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Paper in IBSI 2014 conference entitled “Automated Surgical OSATS Prediction from Videos”

April 28th, 2014 Irfan Essa Posted in Behavioral Imaging, Health Systems, Medical, Papers, Thomas Ploetz, Yachna Sharma No Comments »

  • Y. Sharma, T. Ploetz, N. Hammerla, S. Mellor, R. McNaney, P. Oliver, S. Deshmukh, A. McCaskie, and I. Essa (2014), “Automated Surgical OSATS Prediction from Videos,” in Proceedings of IEEE International Symposium on Biomedical Imaging, Beijing, CHINA, 2014. [PDF] [BIBTEX]
    @InProceedings{    2014-Sharma-ASOPFV,
      address  = {Beijing, CHINA},
      author  = {Yachna Sharma and Thomas Ploetz and Nils Hammerla
          and Sebastian Mellor and Roisin McNaney and Patrick
          Oliver and Sandeep Deshmukh and Andrew McCaskie and
          Irfan Essa},
      booktitle  = {{Proceedings of IEEE International Symposium on
          Biomedical Imaging}},
      month    = {April},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2014-Sharma-ASOPFV.pdf}
          ,
      title    = {Automated Surgical {OSATS} Prediction from Videos},
      year    = {2014}
    }

Abstract

The assessment of surgical skills is an essential part of medical training. The prevalent manual evaluations by expert surgeons are time consuming and often their outcomes vary substantially from one observer to another. We present a video-based framework for automated evaluation of surgical skills based on the Objective Structured Assessment of Technical Skills (OSATS) criteria. We encode the motion dynamics via frame kernel matrices, and represent the motion granularity by texture features. Linear discriminant analysis is used to derive a reduced dimensionality feature space followed by linear regression to predict OSATS skill scores. We achieve statistically significant correlation (p-value < 0.01) between the ground-truth (given by domain experts) and the OSATS scores predicted by our framework.

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Two Ph. D. Defenses the same day. A first for me!

April 2nd, 2014 Irfan Essa Posted in Activity Recognition, Computational Photography and Video, Health Systems, PhD, S. Hussain Raza, Students, Yachna Sharma No Comments »

Today, two of my Ph. D. Students defended their Dissertations.  Back to back.  Congrats to both as they are both done.

Thesis title: Surgical Skill Assessment Using Motion Texture analysis
Student: Yachna Sharma, Ph. D. Candidate in ECE
http://users.ece.gatech.edu/~ysharma3/
Date/Time : 2nd April, 1:00 pm

Title : Temporally Consistent Semantic Segmentation in Videos
S. Hussain Raza, Ph. D. Candidate in ECE
https://sites.google.com/site/shussainraza5/
Date/Time : 2nd April, 1:00 pm

Location : CSIP Library, Room 5186, CenterGy One Building

 

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