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Using MID and high level visual features for surgical workflow detection in cholecystectomy procedures / Sherif Mohamed Hany Shehata ; Supervised Fathi Hassan Saleh , Nicolas Padoy

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Sherif Mohamed Hany Shehata , 2016Description: 53 P. : photographs ; 30cmOther title:
  • استخدام صفات بصرية متوسطة و عالية المستوى لتمييز المراحل الجراحية فى عمليات استئصال المرارة بالمنظار [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering Summary: We present a method that uses visual information in a cholecystectomy procedure{u2019}s video to detect the surgical workflow. While most related work relies on rich external information, we rely only on the endoscopic video used in the surgery. We fine tune a convolutional neural network and use it to get mid-level features representing the surgical phases. Additionally, we train DPM object detectors to detect the used surgical tools, and utilize this information to provide discriminative high-level features. We present a pipeline that employs the mid and high level features by using one vs all SVMs followed by an HHMM to infer the surgical workflow. We present detailed experiments on a relatively large dataset containing 80 cholecystectomy videos. Our best approach achieves 90% detection accuracy in offline mode using only visual information
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2016.Sh.U (Browse shelf(Opens below)) Not for loan 01010110070763000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.06.M.Sc.2016.Sh.U (Browse shelf(Opens below)) 70763.CD Not for loan 01020110070763000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Computer Engineering

We present a method that uses visual information in a cholecystectomy procedure{u2019}s video to detect the surgical workflow. While most related work relies on rich external information, we rely only on the endoscopic video used in the surgery. We fine tune a convolutional neural network and use it to get mid-level features representing the surgical phases. Additionally, we train DPM object detectors to detect the used surgical tools, and utilize this information to provide discriminative high-level features. We present a pipeline that employs the mid and high level features by using one vs all SVMs followed by an HHMM to infer the surgical workflow. We present detailed experiments on a relatively large dataset containing 80 cholecystectomy videos. Our best approach achieves 90% detection accuracy in offline mode using only visual information

Issued also as CD

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