Open Science Article Submitted: January 29, 2016https://doi.org/10.54294/e86siq

Fully Automatic Segmentation of Head and Neck Organs using Active Appearance Models

Richard Mannion-Haworth,Mike Bowes,Annaliese Ashman,Gwenael Guillard,Alan Brett,Graham Vincent1*
1.Imorphics
Abstract

Abstract

We present a fully automatic model based system for segmenting the mandible, parotid and submandibular glands, brainstem, optic nerves and the optic chiasm in CT images, which won the MICCAI 2015 Head and Neck Auto Segmentation Grand Challenge. The method is based on Active Appearance Models (AAM) built from manually segmented examples via a cancer imaging archive provided by the challenge organisers. High quality anatomical correspondences for the models are generated using a Minimum Description Length (MDL) Groupwise Image Registration method. A multi start optimisation scheme is used to robustly match the model to new images. The model has been cross validated on the training data to a good degree of accuracy, and successfully segmented all the test data.

Keywords

SegmentationRadiotherapy
Manuscript
Source Code and Data

Source Code and Data

No source code files available for this publication.

PreviousHead and Neck Auto Segmentation Challenge based on Non-Local Generative Models
NextMulti Atlas Segmentation with Active Shape Model Refinement for Multi-Organ Segmentation in Head and Neck Cancer Radiotherapy Planning