Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching

By Yanrong Guo, Yaozong Gao, Dinggang Shen. 2015

238 citations recorded by the source corpus.

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.

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, background): “example acute ischemic lesion segmentation in DWI [262]; brain tumor segmentation [263]; segmentation of the striatum [264]; segmentation of organs-at-risks in head and neck CT images [265]; and fully automated segmentation of polycystic kidneys [266]; deformable segmentation of the prostate [267]; ”
A survey on deep learning in medical image analysis (2017, methodology): “Guo et al. (2016) Stacked sparse auto-encoders extract features from patches, input to atlas matching and a deformable model”
Modified U-Net (mU-Net) with Incorporation of Object-Dependent High Level Features for Improved Liver and Liver-Tumor Segmentation in CT Images (2019, methodology): “emerged and have been applied for liver [11]–[14], head and neck [15], [16], prostate [17], and brain [18], [19]”
Learning Deep Representations of Medical Images using Siamese CNNs with Application to Content-Based Image Retrieval (2017, methodology): “…image machine learning tasks, such as image classiﬁcation [Esteva et al., 2017, Gulshan et al., 2016], image segmentation [Havaei et al., 2017, Guo et al., 2017], or content-based image retrieval (CBMIR) [Litjens et al., 2017, Sun et al., 2017, Anavi et al., 2016, Liu et al., 2016, Shah et…”
Cancer Diagnosis Using Deep Learning: A Bibliographic Review (2019, background): “Prostate cancer is the third highest cause of death among men and it has a high chance of diagnosing in males [162].”
Big Data and machine learning in radiation oncology: State of the art and future prospects. (2016, unknown): “While DL is gaining interest in medical imaging [46,47] for classification or segmentation, it has not been used to predict the outcome after radiotherapy yet.”
Deep Learning in Medical Image Analysis (2017, methodology): “Another successful medical application is to localize a prostate from MR images (85, 86).”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, unknown): “Another active research in Guo, Gao, and Shen (2015) introduced a model to locate T2 MR prostate images.”