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

Abstract Automatic 3D liver segmentation is a fundamental step in the liver disease diagnosis and surgery planning. This paper presents a novel fully automatic algorithm for 3D liver segmentation in clinical 3D computed tomography (CT) images. Based on image features, we propose a new Mahalanobis distance cost function using an active shape model (ASM). We call our method MD-ASM. Unlike the standard active shape model (ST-ASM), the proposed method introduces a new feature-constrained Mahalanobis distance cost function to measure the distance between the generated shape during the iterative step and the mean shape model. The proposed Mahalanobis distance function is learned from a public database of liver segmentation challenge (MICCAI-SLiver07). As a refinement step, we propose the use of a 3D graph-cut segmentation. Foreground and background labels are automatically selected using texture features of the learned Mahalanobis distance. Quantitatively, the proposed method is evaluated using two clinical 3D CT scan databases (MICCAI-SLiver07 and MIDAS). The evaluation of the MICCAI-SLiver07 database is obtained by the challenge organizers using five different metric scores. The experimental results demonstrate the availability of the proposed method by achieving an accurate liver segmentation compared to the state-of-the-art methods.

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Segmentation method for cardiac region in CT images based on active shape model

ICCAS 2010 ◽

10.1109/iccas.2010.5670140 ◽

2010 ◽

Author(s):

Hiroki Takahashi ◽

Masafumi Komatsu ◽

Hyoungseop Kim ◽

Joo Kooi Tan ◽

Seiji Ishikawa ◽

...

Keyword(s):

Ct Images ◽

Active Shape Model ◽

Segmentation Method ◽

Shape Model ◽

Cardiac Region ◽

Active Shape

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Automatic segmentation of lungs in SPECT images using active shape model trained by meshes delineated in CT images

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2016.7590940 ◽

2016 ◽

Cited By ~ 1

Author(s):

Cheimariotis Grigorios-Aris ◽

Al-Mashat Mariam ◽

Haris Kostas ◽

Aletras H Anthony ◽

Jogi Jonas ◽

...

Keyword(s):

Automatic Segmentation ◽

Ct Images ◽

Active Shape Model ◽

Shape Model ◽

Active Shape ◽

Spect Images

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Validation of active shape model techniques for intra-cochlear anatomy segmentation in CT images

Medical Imaging 2021: Image Processing ◽

10.1117/12.2582096 ◽

2021 ◽

Author(s):

Rueben Banalagay ◽

Robert F. Labadie ◽

Jack H. Noble

Keyword(s):

Ct Images ◽

Active Shape Model ◽

Shape Model ◽

Active Shape

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Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images

Medical Physics ◽

10.1118/1.3515459 ◽

2010 ◽

Vol 37 (12) ◽

pp. 6338-6346 ◽

Cited By ~ 22

Author(s):

Antong Chen ◽

Matthew A. Deeley ◽

Kenneth J. Niermann ◽

Luigi Moretti ◽

Benoit M. Dawant

Keyword(s):

Lymph Node ◽

Head And Neck ◽

Automatic Segmentation ◽

Ct Images ◽

Active Shape Models ◽

Shape Models ◽

Active Shape

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

10.54294/kmcunc ◽

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.

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