Automatic phases recognition in pituitary surgeries by microscope images classification

Florent Lalys 1, * Laurent Riffaud 2 Xavier Morandi 1, 2 Pierre Jannin 1
* Corresponding author
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : The segmentation of the surgical workflow might be helpful for providing context-sensitive user interfaces, or generating automatic report. Our approach focused on the automatic recognition of surgical phases by microscope image classification. Our workflow, including images features extraction, image database labelisation, Principal Component Analysis (PCA) transformation and 10-fold cross-validation studies was performed on a specific type of neurosurgical intervention, the pituitary surgery. Six phases were defined by an expert for this type of intervention. We thus assessed machine learning algorithms along with the data dimension reduction. We finally kept 40 features from the PCA and found a best correct classification rate of the surgical phases of 82% with the multiclass Support Vector Machine.
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Florent Lalys, Laurent Riffaud, Xavier Morandi, Pierre Jannin. Automatic phases recognition in pituitary surgeries by microscope images classification. IPCAI'10, First international conference on Information processing in computer-assisted interventions, Jun 2010, Geneve, Switzerland. pp.34-44, ⟨10.1007/978-3-642-13711-2_4⟩. ⟨inserm-00616977⟩

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