Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching.
Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching.
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DOI：
10.1109/tmi.2015.2508280
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发表时间：
2016-04
影响因子：
10.6
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Guo Y;Gao Y;Shen D
自动和可靠的前列腺分割是一个重要的，但困难的任务，各种临床应用，如前列腺癌放射治疗。精确MR前列腺定位的主要挑战在于两个方面：（1）前列腺边界周围的不均匀和不一致的外观，以及（2）不同患者之间的较大形状变化。为了解决这两个问题，我们提出了一种新的可变形MR前列腺分割方法，将深度特征学习与稀疏补丁匹配相结合。首先，而不是直接使用手工制作的功能，我们建议学习潜在的特征表示从前列腺MR图像的堆叠稀疏自动编码器（SSAE）。由于深度学习算法从数据中学习特征层次结构，因此在描述底层数据时，学习的特征通常比手工特征更简洁有效。为了提高学习特征的可辨别性，我们以监督的方式进一步细化特征表示。其次，基于学习到的特征，提出了一种稀疏补丁匹配方法，通过将前列腺标签从多个图谱转移到新的前列腺MR图像来推断前列腺似然图。最后，一个可变形的分割是用来整合一个稀疏的形状模型与前列腺的可能性地图，以实现最终的分割。所提出的方法已被广泛评估的数据集，包含66 T2加权前列腺MR图像。实验结果表明，在引导MR前列腺分割方面，深度学习的特征比手工特征更有效。此外，我们的方法表现出比其他国家的最先进的分割方法的上级性能。
Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.