Automatic cephalometric evaluation of patients suffering from sleep-disordered breathing

  • Authors:

  • Lior Wolf;Tamir Yedidya;Roy Ganor;Michael Chertok;Ariela Nachmani;Yehuda Finkelstein

  • Affiliations:

  • School of Computer Science, Tel-Aviv University;School of Computer Science, Tel-Aviv University;School of Computer Science, Tel-Aviv University;School of Engineering, Bar-Ilan University;Center of Cleft Palate and Craniofacial Anomalies, Meir Medical Center;Center of Cleft Palate and Craniofacial Anomalies, Meir Medical Center and Department of Otolaryngology, Head and Neck Surgery, Sackler School of Medicine Tel-Aviv University

  • Venue:
  • MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
  • Year:
  • 2010

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Abstract

We address the problem of automatically analyzing lateral cephalometric images as a diagnostic tool for patients suffering from Sleep Disordered Breathing (SDB). First, multiple landmarks and anatomical structures that were previously associated with SDB are localized. Then statistical regression is applied in order to estimate the Respiratory Disturbance Index (RDI), which is the standard measure for the severity of obstructive sleep apnea. The landmark localization employs a new registration method that is based on Local Affine Frames (LAF). Multiple LAFs are sampled per image based on random selection of triplets of keypoints, and are used to register the input image to the training images. The landmarks are then projected from the training images to the query image. Following a refinement step, the tongue, velum and pharyngeal wall are localized. We collected a dataset of 70 images and compare the accuracy of the anatomical landmarks with recent publications, showing preferable performance in localizing most of the anatomical points. Furthermore, we are able to show that the location of the anatomical landmarks and structures predicts the severity of the disorder, obtaining an error of less than 7.5 RDI units for 44% of the patients.