Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models

Authors

  • Li Dengwang,

    1. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Biomedical Sciences, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China and Medical Physics Division, Department of Radiation Oncology, Stanford University, Stanford, California 94305
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  • Zang Pengxiao,

    1. Shandong Province Key Laboratory of Medical Physics and Image Processing Technology, Institute of Biomedical Sciences, School of Physics and Electronics, Shandong Normal University, Jinan 250014, China
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  • Chai Xiangfei,

    1. Medical Physics Division, Department of Radiation Oncology, Stanford University, Stanford, California 94305
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  • Cui Yi,

    1. Medical Physics Division, Department of Radiation Oncology, Stanford University, Stanford, California 94305
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  • Li Ruijiang,

    1. Medical Physics Division, Department of Radiation Oncology, Stanford University, Stanford, California 94305
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  • Xing Lei

    1. Medical Physics Division, Department of Radiation Oncology, Stanford University, Stanford, California 94305
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Abstract

Purpose

Accurate segmentation of pelvic organs in CT images is of great importance in external beam radiotherapy for prostate cancer. The aim of this studying is to develop a novel method for automatic, multiorgan segmentation of the male pelvis.

Methods

The authors’ segmentation method consists of several stages. First, a pretreatment includes parameterization, principal component analysis (PCA), and an established process of region-specific hierarchical appearance cluster (RSHAC) model which was executed on the training dataset. After the preprocessing, online automatic segmentation of new CT images is achieved by combining the RSHAC model with the PCA-based point distribution model. Fifty pelvic CT from eight prostate cancer patients were used as the training dataset. From another 20 prostate cancer patients, 210 CT images were used for independent validation of the segmentation method.

Results

In the training dataset, 15 PCA modes were needed to represent 95% of shape variations of pelvic organs. When tested on the validation dataset, the authors’ segmentation method had an average Dice similarity coefficient and mean absolute distance of 0.751 and 0.371 cm, 0.783 and 0.303 cm, 0.573 and 0.604 cm for prostate, bladder, and rectum, respectively. The automated segmentation process took on average 5 min on a personal computer equipped with Core 2 Duo CPU of 2.8 GHz and 8 GB RAM.

Conclusions

The authors have developed an efficient and reliable method for automatic segmentation of multiple organs in the male pelvis. This method should be useful for treatment planning and adaptive replanning for prostate cancer radiotherapy. With this method, the physicist can improve the work efficiency and stability.

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