Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.
Deep dense multi-path neural network for prostate segmentation in magnetic resonance imaging.
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DOI：
10.1007/s11548-018-1841-4
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发表时间：
2018-11
影响因子：
3
通讯作者：
Kwak JT
中科院分区：
文献类型：
--
作者：
To MNN;Vu DQ;Turkbey B;Choyke PL;Kwak JT
磁共振成像（MRI）中的前列腺自动分割在前列腺癌的诊断和治疗中有着广泛的应用。我们提出了一个深度全卷积神经网络（CNN）来自动分割前列腺。我们的深度CNN模型在基于前列腺MR图像和相应的基础事实的单个学习阶段中进行端到端训练，并学习为像素分割进行推断。实验是在我们的内部数据集上进行的，该数据集包含20例患者的前列腺MR图像。与手动分割相比，所提出的CNN模型获得了85.3%±3.2%的平均Dice相似系数。实验结果表明，我们的深度CNN模型可以得到令人满意的前列腺分割。
Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3%±3.2% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.
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通讯作者：
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