scholarly journals Multi Atlas Segmentation with Active Shape Model Refinement for Multi-Organ Segmentation in Head and Neck Cancer Radiotherapy Planning

2015 ◽  
Author(s):  
Thomas Albrecht ◽  
Tobias Gass ◽  
Christoph Langguth ◽  
Marcel Lüthi
Keyword(s):  
Head And Neck ◽  
Organs At Risk ◽  
Building Blocks ◽  
Training Data ◽  
Radiotherapy Planning ◽  
Active Shape Model ◽  
Shape Model ◽  
Active Shape ◽  
Initial Segmentation ◽  
Atlas Segmentation

We describe a segmentation method that was used in the Head and Neck Auto Segmentation Challenge held at the MICCAI 2015 conference. The algorithm consists of two building blocks. First, we employ a multi-atlas segmentation to obtain an initial segmentation for the considered organs at risk. Secondly, we use an Active Shape Model (ASM) segmentation to refine the initial segmentation of some of the organs. Leave-one-out experiments with the training data were used to determine suitable parameters for the individual steps of the segmentation. The ASM refinement resulted in improved segmentation for the optic nerves and submandibular glands, while for the brain stem, parotid glands, chiasm, and mandibular bone, the multi-atlas segmentation was preferable. Our submission achieved the second rank in the challenge.

Segmentation of parotid glands in head and neck CT images using a constrained active shape model with landmark uncertainty

2012 ◽  
Author(s):  
Antong Chen ◽  
Jack H. Noble ◽  
Kenneth J. Niermann ◽  
Matthew A. Deeley ◽  
Benoit M. Dawant
Keyword(s):  
Head And Neck ◽  
Ct Images ◽  
Active Shape Model ◽  
Parotid Glands ◽  
Shape Model ◽  
Active Shape

Segmentation of lymph node regions in head-and-neck CT images using a combination of registration and active shape model

2010 ◽  
Author(s):  
Antong Chen ◽  
Matthew A. Deeley ◽  
Kenneth J. Niermann ◽  
Luigi Moretti ◽  
Benoit M. Dawant
Keyword(s):  
Lymph Node ◽  
Head And Neck ◽  
Ct Images ◽  
Active Shape Model ◽  
Shape Model ◽  
Active Shape

scholarly journals The interleaved partial active shape model search (IPASM) algorithm – Preliminary results of a novel approach towards 3D ultrasound-based bone surface reconstruction

10.29007/ckw2 ◽  
2018 ◽  
Author(s):  
Christoph Hänisch ◽  
Benjamin Hohlmann ◽  
Klaus Radermacher
Keyword(s):  
Search Algorithm ◽  
Imaging Modality ◽  
Statistical Shape Model ◽  
Training Data ◽  
Active Shape Model ◽  
Imaging Data ◽  
Shape Model ◽  
Underlying Distribution ◽  
Model Search ◽  
Active Shape

In applications such as biomechanical simulations or implant planning, bone surfaces of the knee are most often reconstructed from computed tomography or magnetic resonance imaging data. Here, ultrasound (US) might serve as an alternative imaging modality. However, established methods cannot directly be transferred to US due to differences in imaging quality and underlying physics.In this paper, we present a generalisation of the well-known active shape model search algorithm (ASM) that allows for segmenting various structures in US volume images that are too large to be captured with a single recording. The multi-view segmentation approach uses a-priori knowledge in the form of a statistical shape model (SSM) as is the case with the classical ASM. This allows to extrapolate missing information and to generate shapes that comply with the underlying distribution of some training data. The main differences are, however, that the SSM is not only adapted to a single image but to multiple images and that the adaption process is interleaved. As a result, within each iteration the surface information of all sub-volumes is propagated and used in all subsequent steps.In-silico tests were conducted to investigate how this algorithm would perform in real tracked US data. US volume images were split in slightly overlapping sub-volumes, noise was added, and the alignment was distorted. We could show that the algorithm is capable of reconstructing shapes in the lower millimetre range and for some cases even with submillimetric accuracy. The algorithm is hardly affected by orientation errors below 5 degrees and displacement errors below 5 mm; above these limits, the average absolute SDE as well as its associated variance increases.

SU-GG-I-99: Registration and Active Shape Model-Based Segmentation of Lymph Node Regions in Head and Neck CT Images

2010 ◽  
Vol 37 (6Part4) ◽  
pp. 3124-3124
Author(s):  
A Chen ◽  
M Deeley ◽  
K Niermann ◽  
L Moretti ◽  
B Dawant
Keyword(s):  
Lymph Node ◽  
Head And Neck ◽  
Ct Images ◽  
Active Shape Model ◽  
Shape Model ◽  
Model Based ◽  
Active Shape

scholarly journals A Heuristic Image Search Algorithm for Active Shape Model Segmentation of the Caudate Nucleus and Hippocampus in Brain MR Images of Children with FASD

2012 ◽  
Vol 49 ◽  
Author(s):  
A A Eicher ◽  
P Marais ◽  
C Warton ◽  
S W Jacobson ◽  
J L Jacobson ◽  
...  
Keyword(s):  
Caudate Nucleus ◽  
Search Algorithm ◽  
Heuristic Method ◽  
Neural Correlates ◽  
Training Data ◽  
Active Shape Model ◽  
Image Search ◽  
Mr Images ◽  
Shape Model ◽  
Active Shape

Magnetic Resonance Imaging provides a non-invasive means to study the neural correlates of Fetal Alcohol Spectrum Disorder (FASD) - the most common form of preventable mental retardation worldwide. One approach aims to detect brain abnormalities through an assessment of volume and shape of two sub-cortical structures, the caudate nucleus and hippocampus. We present a method for automatically segmenting these structures from high-resolution MR images captured as part of an ongoing study into the neural correlates of FASD. Our method incorporates an Active Shape Model, which is used to learn shape variation from manually segmented training data. A modified discrete Geometrically Deformable Model is used to generate point correspondence between training models. An ASM is then created from the landmark points. Experiments were conducted on the image search phase of ASM segmentation, in order to find the technique best suited to segmentation of the hippocampus and caudate nucleus. Various popular image search techniques were tested, including an edge detection method and a method based on grey profile Mahalanobis distance measurement. A novel heuristic image search method was also developed and tested. This heuristic method improves image segmentation by taking advantage of characteristics specific to the target data, such as a relatively homogeneous tissue colour in target structures. Results show that ASMs that use the heuristic image search technique produce the most accurate segmentations. An ASM constructed using this technique will enable researchers to quickly, reliably, and automatically segment test data for use in the FASD study.

Human face shape classification method based on active shape model

2009 ◽  
Vol 29 (10) ◽  
pp. 2710-2712 ◽  
Author(s):  
Li-qiang DU ◽  
Peng JIA ◽  
Zong-tan ZHOU ◽  
De-wen HU
Keyword(s):  
Classification Method ◽  
Active Shape Model ◽  
Shape Classification ◽  
Human Face ◽  
Shape Model ◽  
Face Shape ◽  
Active Shape

Echocardiogram segmentation using active shape model and mean squared eigenvalue error

2021 ◽  
Vol 69 ◽  
pp. 102807
Author(s):  
Yasser Ali ◽  
Soosan Beheshti ◽  
Farrokh Janabi-Sharifi
Keyword(s):  
Active Shape Model ◽  
Shape Model ◽  
Active Shape

Three-dimensional active shape model matching for left ventricle segmentation in cardiac CT

2003 ◽  
Author(s):  
Hans C. van Assen ◽  
Rob J. van der Geest ◽  
Mikhail G. Danilouchkine ◽  
Hildo J. Lamb ◽  
Johan H. C. Reiber ◽  
...  
Keyword(s):  
Left Ventricle ◽  
Cardiac Ct ◽  
Three Dimensional ◽  
Active Shape Model ◽  
Model Matching ◽  
Shape Model ◽  
Active Shape ◽  
Ventricle Segmentation
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