International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2014: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 pp 676-683 | Cite as

Automatic Labelling of Tumourous Frames in Free-Hand Laparoscopic Ultrasound Video

  • Jeremy Kawahara
  • Jean-Marc Peyrat
  • Julien Abinahed
  • Osama Al-Alao
  • Abdulla Al-Ansari
  • Rafeef Abugharbieh
  • Ghassan Hamarneh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Laparoscopic ultrasound (US) is often used during partial nephrectomy surgeries to identify tumour boundaries within the kidney. However, visual identification is challenging as tumour appearance varies across patients and US images exhibit significant noise levels. To address these challenges, we present the first fully automatic method for detecting the presence of kidney tumour in free-hand laparoscopic ultrasound sequences in near real-time. Our novel approach predicts the probability that a frame contains tumourous tissue using random forests and encodes this probability combined with a regularization term within a graph. Using Dijkstra’s algorithm we find a globally optimal labelling (tumour vs. non-tumour) of each frame. We validate our method on a challenging clinical dataset composed of five patients, with a total of 2025 2D ultrasound frames, and demonstrate the ability to detect the presence of kidney tumour with a sensitivity and specificity of 0.774 and 0.916, respectively.

Keywords

Random Forest Partial Nephrectomy Regularization Term Open Partial Nephrectomy Temporal Regularization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jeremy Kawahara
    • 1
  • Jean-Marc Peyrat
    • 2
  • Julien Abinahed
    • 2
  • Osama Al-Alao
    • 3
  • Abdulla Al-Ansari
    • 2
    • 3
  • Rafeef Abugharbieh
    • 4
  • Ghassan Hamarneh
    • 1
  1. 1.Medical Image Analysis LabSimon Fraser UniversityBurnabyCanada
  2. 2.Qatar Robotic Surgery CentreQatar Science & Technology ParkDohaQatar
  3. 3.Urology Department, Hamad General HospitalHamad Medical CorporationQatar
  4. 4.BiSICLUniversity of British ColumbiaVancouverCanada

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