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

2016 ◽  
Author(s):  
Richard Mannion-Haworth ◽  
Mike Bowes ◽  
Annaliese Ashman ◽  
Gwenael Guillard ◽  
Alan Brett ◽  
...  
Keyword(s):  
Head And Neck ◽  
Minimum Description Length ◽  
Automatic Segmentation ◽  
Training Data ◽  
Grand Challenge ◽  
Active Appearance Models ◽  
Registration Method ◽  
Appearance Models ◽  
Fully Automatic ◽  
Active Appearance

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.

AUTOMATIC SEGMENTATION OF CARDIAC MAGNETIC RESONANCE IMAGES USING ACTIVE APPEARANCE MODELS AND HAUSDORFF DISTANCE

2012 ◽  
Vol 12 (04) ◽  
pp. 1250059
Author(s):  
MOHAMMED AMMAR ◽  
SAÏD MAHMOUDI ◽  
MOHAMMED AMINE CHIKH ◽  
AMINE ABBOU
Keyword(s):  
Cardiac Magnetic Resonance ◽  
Magnetic Resonance ◽  
Hausdorff Distance ◽  
Automatic Segmentation ◽  
Initial Position ◽  
Magnetic Resonance Images ◽  
Test Image ◽  
Active Appearance Models ◽  
Appearance Models ◽  
Active Appearance

Active Appearance Models (AAM), have been introduced by Cootes et al. [IEEE Transactions on Pattern Analysis and Machine Intelligence, 2001], and are used to learn objects characteristics during a training phase by building a compact statistical model representing shape and texture variation of the object. This Model is used to find the object location and shape-appearance parameters, in a test set. The selection of the initial position of the construct model in a test image is a very important task in this context. The goal of this work is to propose an automatic segmentation method applied to cardiovascular MR images using an AAM based segmentation approach. The AAM model was constructed using 20 end-diastolic and end-systolic short axis cardiac magnetic resonance images (MRI). Once the model is constructed, we select the best position in order to start the search step manually in the test image. That is why; in this paper, the localization of the left ventricular cavity in the test image is used to select the initial position of the construct model developed from the training images. So we propose an automatic approach to detect this spatial position by using two methods: (1) the circular Hough transform (CHT) and (2) the evaluation of the Hausdorff distance.

scholarly journals Segmentation of Multi-Center 3D Left Ventricular Echocardiograms by Active Appearance Models

2014 ◽  
Author(s):  
Marijn Van Stralen ◽  
Alexander Haak ◽  
K Esther Leung ◽  
Gerard Van Burken ◽  
Johan G. Bosch
Keyword(s):  
Signal To Noise Ratio ◽  
Left Ventricular ◽  
Training Data ◽  
Active Appearance Models ◽  
Segmentation Accuracy ◽  
Testing Data ◽  
Appearance Models ◽  
Endocardial Surface ◽  
Active Appearance ◽  
Leave One Out

Segmentation of 3D echocardiograms (3DEs) is still a challenging task due to the low signal-to-noise ratio, the limited field of view, and typical ultrasound artifacts. We propose to segment the left ventricular endocardial surface by using Active Appearance Models (AAMs). Separate end-diastolic (ED) and end-systolic (ES) AAMs were built from presegmented 3DEs of the CETUS training data and 25 previously acquired 3DEs, imaged using various 3DE equipment. The AAMs fully automatically segmented the 15 training sets in a leave-one-out cross validation, comparing two training populations and various initialization strategies. All segmentations took about 15 seconds per patient. The comparison on the CETUS training data shows that the AAM benefits from additional training data and more accurate initialization. The results on the CETUS training and testing data confirm good ED and ES segmentation accuracy on multi-center, multi-vendor, multi-pathology data, and corresponding EF estimation. Selection from different initialization strategies, based on the minimal residual error, and propagation of detected ED contours to initialize ES detection, contributed to more accurate segmentations in this heterogeneous population.

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