Automatic segmentation of prostate in MR images using deep learning and multi-atlas techniques

机译：使用深度学习和多图谱技术对MR图像中的前列腺进行自动分割

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Precise segmentation of prostate in magnetic resonance images is an essential step in treatment planning and a challenging task due to high variability in shape and size of the tissue. In this paper, we propose an automatic algorithm for accurate and robust segmentation of prostate in MR images. First, we employ a deep neural network to locate the prostate region of interest which removes background pixels and reduces the size of the image. Then, we obtain an initial segmentation of the tissue using a probabilistic atlas. Finally, we utilize statistical shape models to restrict the final contour inside the allowable shape domain. We performed a quantitative evaluation on 30 MR images and obtained a mean Dice similarity coefficient of 0.85±0.06. Compared to recent researches, our method is both robust and accurate.

机译：由于组织形状和大小的高度可变性，在磁共振图像中对前列腺进行精确分割是治疗计划中必不可少的步骤，也是一项艰巨的任务。在本文中，我们提出了一种自动算法，可对MR图像中的前列腺进行精确而鲁棒的分割。首先，我们采用深度神经网络来定位目标前列腺区域，该区域会去除背景像素并减小图像尺寸。然后，我们使用概率图集获得组织的初始分割。最后，我们利用统计形状模型将最终轮廓限制在允许的形状域内。我们对30张MR图像进行了定量评估，得出的平均Dice相似系数为0.85±0.06。与最近的研究相比，我们的方法既稳健又准确。

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《National Conference on Biomedical Engineering;International Iranian Conference on Biomedical Engineering》|2018年|1-4|共4页
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Hamid Moradi; Amir Hossein Foruzan; Yen-Wei Chen;
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Image segmentation; Shape; Probabilistic logic; Active appearance model; Biomedical engineering; Training; Training data;

机译：图像分割形状概率逻辑活动外观模型生物医学工程训练训练数据;

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1. AUTOMATIC SEGMENTATION OF HIPPOCAMPAL SUBFIELDS BASED ON MULTI-ATLAS IMAGE SEGMENTATION TECHNIQUES [J] . Shi Yonggang, Zhang Xueping, Liu Zhiwen 电子科学学刊（英文版） . 2014,第002期

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2. Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach [J] . Brent van der Heyden, Patrick Wohlfahrt, Dani?lle B. P. Eekers, Scientific reports. . 2019,第1期

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3. Dual-energy CT for automatic organs-at-risk segmentation in brain-tumor patients using a multi-atlas and deep-learning approach [J] . Brent van der Heyden, Patrick Wohlfahrt, Dani?lle B. P. Eekers, Scientific reports. . 2019,第1期

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4. Automatic segmentation of prostate in MR images using deep learning and multi-atlas techniques [C] . Hamid Moradi, Amir Hossein Foruzan, Yen-Wei Chen National Iranian Conference on Biomedical Engineering . 2018

机译：利用深度学习和多拟标技术自动分割先生图像中的前列腺
5. Brain MR Image Segmentation: From Multi-Atlas Method to Deep Learning Models [D] . Huo, Jie. 2019

机译：脑MR图像分割：从多拟标方法到深度学习模型
6. Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion [O] . Ling Ma, Rongrong Guo, Guoyi Zhang, -1

机译：使用深度学习和多图谱融合在CT图像上自动分割前列腺
7. Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images [O] . Nathan Orlando, Derek J. Gillies, Igor Gyacskov, 2020

机译：自动前列腺分割在临床上使用深度学习临床不同的3D经纬超声图像

Automatic segmentation of prostate in MR images using deep learning and multi-atlas techniques

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