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Real-Time Segmentation and Recognition of Surgical Tasks in Cataract Surgery Videos.

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Title: Real-Time Segmentation and Recognition of Surgical Tasks in Cataract Surgery Videos.
Authors: Quellec, Gwenole1, Lamard, Mathieu1, Cochener, Beatrice1, Cazuguel, Guy1
Source: IEEE Transactions on Medical Imaging. Dec2014, Vol. 33 Issue 12, p2352-2360. 9p.
Subjects: Cataract surgery, Image segmentation, Content-based image retrieval, Random fields, Hidden Markov models
Abstract: In ophthalmology, it is now common practice to record every surgical procedure and to archive the resulting videos for documentation purposes. In this paper, we present a solution to automatically segment and categorize surgical tasks in real-time during the surgery, using the video recording. The goal would be to communicate information to the surgeon in due time, such as recommendations to the less experienced surgeons. The proposed solution relies on the content-based video retrieval paradigm: it reuses previously archived videos to automatically analyze the current surgery, by analogy reasoning. Each video is segmented, in real-time, into an alternating sequence of idle phases, during which no clinically-relevant motions are visible, and action phases. As soon as an idle phase is detected, the previous action phase is categorized and the next action phase is predicted. A conditional random field is used for categorization and prediction. The proposed system was applied to the automatic segmentation and categorization of cataract surgery tasks. A dataset of 186 surgeries, performed by ten different surgeons, was manually annotated: ten possibly overlapping surgical tasks were delimited in each surgery. Using the content of action phases and the duration of idle phases as sources of evidence, an average recognition performance of Az = 0.832 \pm 0.070 was achieved. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="DE" term="%22Cataract+surgery%22">Cataract surgery</searchLink><br /><searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Content-based+image+retrieval%22">Content-based image retrieval</searchLink><br /><searchLink fieldCode="DE" term="%22Random+fields%22">Random fields</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink>
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  Data: In ophthalmology, it is now common practice to record every surgical procedure and to archive the resulting videos for documentation purposes. In this paper, we present a solution to automatically segment and categorize surgical tasks in real-time during the surgery, using the video recording. The goal would be to communicate information to the surgeon in due time, such as recommendations to the less experienced surgeons. The proposed solution relies on the content-based video retrieval paradigm: it reuses previously archived videos to automatically analyze the current surgery, by analogy reasoning. Each video is segmented, in real-time, into an alternating sequence of idle phases, during which no clinically-relevant motions are visible, and action phases. As soon as an idle phase is detected, the previous action phase is categorized and the next action phase is predicted. A conditional random field is used for categorization and prediction. The proposed system was applied to the automatic segmentation and categorization of cataract surgery tasks. A dataset of 186 surgeries, performed by ten different surgeons, was manually annotated: ten possibly overlapping surgical tasks were delimited in each surgery. Using the content of action phases and the duration of idle phases as sources of evidence, an average recognition performance of Az = 0.832 \pm 0.070 was achieved. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TMI.2014.2340473
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        Text: English
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        Type: general
      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Content-based image retrieval
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      – SubjectFull: Random fields
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      – SubjectFull: Hidden Markov models
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              M: 12
              Text: Dec2014
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