scholarly journals A Multi-atlas Approach for the Automatic Segmentation of Multiple Structures in Head and Neck CT Images

2016 ◽  
Author(s):  
Antong Chen ◽  
Benoit Dawant
Keyword(s):  
Head And Neck ◽  
Local Level ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Training Dataset ◽  
Parotid Glands ◽  
Label Fusion ◽  
Qualitative And Quantitative ◽  
Segmentation Accuracy ◽  
Multiple Structures

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).

Automatic Parotid Gland Segmentation in MVCT Using Deep Convolutional Neural Networks

2022 ◽  
Vol 3 (2) ◽  
pp. 1-15
Author(s):  
Junqian Zhang ◽  
Yingming Sun ◽  
Hongen Liao ◽  
Jian Zhu ◽  
Yuan Zhang
Keyword(s):  
Parotid Gland ◽  
Head And Neck ◽  
Helical Tomotherapy ◽  
Radiation Treatment ◽  
Automatic Segmentation ◽  
Parotid Glands ◽  
Deep Convolutional Neural Networks ◽  
Megavoltage Computed Tomography ◽  
Radiation Induced ◽  
Segmentation Models

Radiation-induced xerostomia, as a major problem in radiation treatment of the head and neck cancer, is mainly due to the overdose irradiation injury to the parotid glands. Helical Tomotherapy-based megavoltage computed tomography (MVCT) imaging during the Tomotherapy treatment can be applied to monitor the successive variations in the parotid glands. While manual segmentation is time consuming, laborious, and subjective, automatic segmentation is quite challenging due to the complicated anatomical environment of head and neck as well as noises in MVCT images. In this article, we propose a localization-refinement scheme to segment the parotid gland in MVCT. After data pre-processing we use mask region convolutional neural network (Mask R-CNN) in the localization stage after data pre-processing, and design a modified U-Net in the following fine segmentation stage. To the best of our knowledge, this study is a pioneering work of deep learning on MVCT segmentation. Comprehensive experiments based on different data distribution of head and neck MVCTs and different segmentation models have demonstrated the superiority of our approach in terms of accuracy, effectiveness, flexibility, and practicability. Our method can be adopted as a powerful tool for radiation-induced injury studies, where accurate organ segmentation is crucial.

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

scholarly journals Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

2020 ◽  
Author(s):  
Wen Chen ◽  
Yimin Li ◽  
Brandon A Dyer ◽  
Xue Feng ◽  
Shyam Rao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Head And Neck ◽  
Qualitative Difference ◽  
Masticatory Muscles ◽  
Ct Images ◽  
Mean Value ◽  
Dice Similarity Coefficient ◽  
Segmentation Accuracy ◽  
The Mean ◽  
Deep Learning Model

Abstract Background: Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images. Material and methods: Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n=27) and validation (n=29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods.Results: DLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83±0.03 to 0.89±0.02, the ABAS mean DSC ranged from 0.79±0.05 to 0.85±0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ∆D98%, ∆D95%, ∆D50%, and ∆D2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P >0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS. Conclusions: DLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

scholarly journals Head and Neck Auto Segmentation Challenge based on Non-Local Generative Models

2016 ◽  
Author(s):  
Mauricio Orbes Arteaga ◽  
David Cárdenas Peña ◽  
German Castellanos Dominguez
Keyword(s):  
Head And Neck ◽  
Optic Chiasm ◽  
Ct Images ◽  
Generative Models ◽  
The Other ◽  
Fusion Method ◽  
Probabilistic Criterion ◽  
Label Fusion ◽  
Non Local ◽  
Generative Approach

A new patch based label fusion method based on generative approach is proposed for segmentation of mandible, brainstem, parotid and submandibular glands, optic nerves and the optic chiasm in head and neck CT images. The proposal constructs local classifiers from a dictionary of patches and weights their contribution using a generative probabilistic criterion. Also, a gaussian slide window is used to weight the multiples estimations of neighboring voxels. The proposed method was evaluated on a set of 15 CT images (10 off-site and 5 onsite) provided by the organizers of the Head and neck Auto-Segmentation challenge(MICCAI 2015), where the obtained results are comparable to many of the other methods used in the challenge.

scholarly journals Automatic Segmentation of Structures in CT Head and Neck Images using a Coupled Shape Model

2016 ◽  
Author(s):  
Florian Jung ◽  
Oliver Knapp ◽  
Stefan Wesarg
Keyword(s):  
Head And Neck ◽  
Automatic Segmentation ◽  
Data Sets ◽  
Model Adaptation ◽  
Shape Variation ◽  
Adaptation Algorithm ◽  
Shape Model ◽  
Training Samples ◽  
Statistical Shape ◽  
Multiple Structures

The common approach to do a fully automatic segmentation of multiple structures is an atlas or multi-atlas based solution. These already have proven to be suitable for the segmentation of structures in the head and neck area and provide very accurate segmentation results, but can struggle with challenging cases with unnatural postures, where the registration of the reference patient(s) is extremely difficult. Therefore, we propose an coupled shape model (CoSMo) algorithm for the segmentation relevant structures in parallel. The model adaptation to a test image is done with respect to the appearance of its items and the trained articulation space. Even on very challenging data sets with unnatural postures, which occur far more often than expected, the model adaptation algorithm succeeds. The approach is based on an articulated atlas , that is trained from a set of manually labeled training samples. Furthermore, we have combined the initial solution with statistical shape models to represent structures with high shape variation. CoSMo is not tailored to specific structures or regions. It can be trained from any set of given gold standard segmentations and makes it thereby very generic.

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

2010 ◽  
Vol 78 (3) ◽  
pp. S490-S491 ◽  
Author(s):  
D.T. Gering ◽  
W. Lu ◽  
K. Ruchala ◽  
G. Olivera
Keyword(s):  
Head And Neck ◽  
Automatic Segmentation ◽  
Ct Images

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

2010 ◽  
Vol 37 (12) ◽  
pp. 6338-6346 ◽  
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

scholarly journals Deep learning vs. atlas-based models for fast auto-segmentation of the masticatory muscles on head and neck CT images

2020 ◽  
Author(s):  
Wen Chen ◽  
Brandon A Dyer ◽  
Xue Feng ◽  
Yimin Li ◽  
Shyam Rao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Head And Neck ◽  
Qualitative Difference ◽  
Masticatory Muscles ◽  
Ct Images ◽  
Mean Value ◽  
Dice Similarity Coefficient ◽  
Segmentation Accuracy ◽  
The Mean ◽  
Deep Learning Model

Abstract Background: Trismus is caused by impaired function of masticatory muscles. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model vs. a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images. Material and methods: Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n=27) and validation (n=29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). Quantitative assessment of automatic versus manually segmented contours were performed using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). The interobserver variability in manual segmentation of MMs was also evaluated. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods.Results: DLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged between 0.83±0.03 to 0.89±0.02, the ABAS mean DSC ranged between 0.79±0.05 to 0.85±0.04. The mean value for recall, precision, HD, HD95, MSD also improved with DLAS for auto-segmentation and were close to the mean interobserver variation. With few exceptions, ∆D99%, ∆D95%, ∆D50%, and ∆D1% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P >0.05). DLAS based contours have dose endpoints more closely matched with that of the manually segmented when compared with ABAS. Conclusions: DLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

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