Hyperthermia critical tissues automatic segmentation of head and neck CT images using atlas registration and graph cuts

A multi-atlas approach is proposed for the automatic segmentation of nine different structures in a set of head and neck CT images for radiotherapy. The approach takes advantage of a training dataset of 25 images to build average head and neck atlases of high-quality. By registering patient images with the atlases at the global level, structures of interest are aligned approximately in space, which allowed multi-atlas-based segmentations and correlation-based label fusion to be performed at the local level in the following steps. Qualitative and quantitative evaluations are performed on a set of 15 testing images. As shown by the results, mandible, brainstem and parotid glands are segmented accurately (mean volume DSC>0.8). The segmentation accuracy for the optic nerves is also improved over previously reported results (mean DSC above 0.61 compared with 0.52 for previous results).

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Automatic Segmentation of Head and Neck CT Images by GPU-Accelerated Multi-atlas Fusion

10.54294/5oitxb ◽

2009 ◽

Author(s):

Xiao Han ◽

Xiaodong Han ◽

Lyndon Hibbard ◽

Nicolette O'Connell ◽

Virgil Willcut

Keyword(s):

Head And Neck ◽

Automatic Segmentation ◽

Grand Challenge ◽

Segmentation Method ◽

3D Segmentation ◽

Atlas Registration ◽

Precision Radiotherapy ◽

Atlas Segmentation ◽

High Precision Radiotherapy ◽

Segmentation Problem

Treatment planning for high precision radiotherapy of head and neck (H&N) cancer patients requires accurate delineation of critical structures. Manual contouring is tedious and often suffers from large inter- and intra-rater variability. In this paper, we present a fully automated, atlas-based segmentation method and apply it to tackle the H&N CT image segmentation problem in the MICCAI 2009 3D Segmentation Grand Challenge. The proposed method employs a multiple atlas fusion strategy and a hierarchical atlas registration approach. We also exploit recent advancements in GPU technology to accelerate the deformable atlas registration and to make multi-atlas segmentation computationally feasible in practice. Validation results on the eight clinical datasets distributed by the MICCAI workshop showed that the proposed method gave very accurate segmentation of the mandible and the brainstem, with a volume overlap close to or above 90% for most subjects. These results suggest that our method is clinically applicable, accurate, and may significantly reduce manual labor and improve contouring efficiency.

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Automatic Segmentation of Renal Parenchyma using Graph-cuts with Shape Constraint based on Multi-probabilistic Atlas in Abdominal CT Images

Journal of the Korea Computer Graphics Society ◽

10.15701/kcgs.2016.22.4.11 ◽

2016 ◽

Vol 22 (4) ◽

pp. 11-19

Author(s):

이재선 ◽

홍헬렌 ◽

나군호

Keyword(s):

Automatic Segmentation ◽

Graph Cuts ◽

Ct Images ◽

Renal Parenchyma ◽

Abdominal Ct ◽

Shape Constraint ◽

Probabilistic Atlas

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Automatic Segmentation of KV-CT and MV-CT Images of Head and Neck Patients

International Journal of Radiation Oncology*Biology*Physics ◽

10.1016/j.ijrobp.2010.07.1148 ◽

2010 ◽

Vol 78 (3) ◽

pp. S490-S491 ◽

Cited By ~ 1

Author(s):

D.T. Gering ◽

W. Lu ◽

K. Ruchala ◽

G. Olivera

Keyword(s):

Head And Neck ◽

Automatic Segmentation ◽

Ct Images

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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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PO-1655 Tuning deep learning models for automatic segmentation of head and neck cancers in PET/CT images

Radiotherapy and Oncology ◽

10.1016/s0167-8140(21)08106-8 ◽

2021 ◽

Vol 161 ◽

pp. S1374-S1376

Author(s):

B.N. Huynh ◽

A.R. Groendahl ◽

Y.M. Moe ◽

O. Tomic ◽

E. Dale ◽

...

Keyword(s):

Deep Learning ◽

Head And Neck ◽

Automatic Segmentation ◽

Ct Images ◽

Head And Neck Cancers ◽

Learning Models ◽

Pet Ct

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A deep learning method for automatic segmentation of the bony orbit in MRI and CT images

Scientific Reports ◽

10.1038/s41598-021-93227-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jared Hamwood ◽

Beat Schmutz ◽

Michael J. Collins ◽

Mark C. Allenby ◽

David Alonso-Caneiro

Keyword(s):

Deep Learning ◽

Human Performance ◽

Automatic Segmentation ◽

Ct Images ◽

Human Observer ◽

In Series ◽

Magnetic Resonance Imaging Mri ◽

High Degree ◽

Orbital Region ◽

Ct And Mri

AbstractThis paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance imaging (MRI) and computed tomography (CT). The method, based on a deep learning architecture, uses two fully convolutional neural networks in series followed by a graph-search method to generate a boundary for the orbit. When compared to human performance for segmentation of both CT and MRI data, the proposed method achieves high Dice coefficients on both orbit and background, with scores of 0.813 and 0.975 in CT images and 0.930 and 0.995 in MRI images, showing a high degree of agreement with a manual segmentation by a human expert. Given the volumetric characteristics of these imaging modalities and the complexity and time-consuming nature of the segmentation of the orbital region in the human skull, it is often impractical to manually segment these images. Thus, the proposed method provides a valid clinical and research tool that performs similarly to the human observer.

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