Prostate Segmentation by Sparse Representation Based Classification

Gao, Yaozong; Liao, Shu; Shen, Dinggang

doi:10.1007/978-3-642-33454-2_56

Cited by 21 publications

(37 citation statements)

References 17 publications

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“…The manual delineation of the prostate in each image is provided by a clinical expert as the ground truth for quantitative evaluation. As the preprocessing of the dataset, the bias field correction [46] and histogram matching [47] are applied to each image successively. We adopted the two-fold cross-validation.…”

Section: A Materials and Parameter Settingsmentioning

confidence: 99%

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

Guo

Gao

Shen

2017

Deep Learning for Medical Image Analysis

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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.Correspondence to: Dinggang Shen, dgshen@med.unc.edu. † Co-first authors. HHS Public Access

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Section: A Materials and Parameter Settingsmentioning

confidence: 99%

“…13 (d)). Specifically, for the case of handcrafted features, we include three commonly used features, i.e., Haar [50], HoG [47] and LBP [21]. The same patch size is used for computing all features under comparison.…”

Section: ) Evaluation Of the Performance Of Firstmentioning

confidence: 99%

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

Guo

Gao

Shen

2017

Deep Learning for Medical Image Analysis

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“…However, the classification is performed for each voxel independently, and also as noticed in our previous work , the probability maps obtained by the random forest might introduce artificial anatomical errors in the final segmentation results. To deal with this possible limitation, we impose an anatomical constraint into the segmentation by using sparse representation, which has been employed in many applications (Gao et al, 2012;Shao et al, 2014;Wang et al, 2013b;Wang et al, 2014b). Specifically, by applying the trained classification forests, each training subject i can obtain its corresponding forest-based tissue probability maps .…”

Section: Post-processing: Imposing Anatomical Constraint Into the Segmentioning

confidence: 99%

LINKS: Learning-Based Multi-source IntegratioN FrameworK for Segmentation of Infant Brain Images

Wang

Gao

Shi

et al. 2014

Medical Computer Vision: Algorithms for Big Data

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Segmentation of infant brain MR images is challenging due to insufficient image quality, severe partial volume effect, and ongoing maturation and myelination processes. In the first year of life, the image contrast between white and gray matters of the infant brain undergoes dramatic changes. In particular, the image contrast is inverted around 6-8 months of age, and the white and gray matter tissues are isointense in both T1-and T2-weighted MR images and thus exhibit the extremely low tissue contrast, which poses significant challenges for automated segmentation. Most previous studies used multi-atlas label fusion strategy, which has the limitation of equally treating the different available image modalities and is often computationally expensive. To cope with these limitations, in this paper, we propose a novel learning-based multi-source integration framework for segmentation of infant brain images. Specifically, we employ the random forest technique to effectively integrate features from multi-source images together for tissue segmentation. Here, the multi-source images include initially only the multi-modality (T1, T2 and FA) images and later also the iteratively estimated and refined tissue probability maps of gray matter, white matter, and cerebrospinal fluid. Experimental results on 119 infants show that the proposed method achieves better performance than other state-of-the-art automated segmentation methods. Further validation was performed on the MICCAI grand challenge and the proposed method was ranked top among all competing methods. Moreover, to alleviate the possible Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. anatomical errors, our method can also be combined with an anatomically-constrained multi-atlas labeling approach for further improving the segmentation accuracy. NIH Public Access

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“…43 DSC, sensitivity, and PPV are used as metrics to evaluate the spatial overlap agreement between manual segmentation and automated segmentation, in which higher values indicate more accurate segmentations. ASD is a surface-based performance measure calculated as the average distance between the manually segmented shape and the automatically segmented shape.…”

Section: B Evaluation Criteriamentioning

confidence: 99%

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

et al. 2014

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Purpose: Automatic prostate segmentation from MR images is an important task in various clinical applications such as prostate cancer staging and MR-guided radiotherapy planning. However, the large appearance and shape variations of the prostate in MR images make the segmentation problem difficult to solve. Traditional Active Shape/Appearance Model (ASM/AAM) has limited accuracy on this problem, since its basic assumption, i.e., both shape and appearance of the targeted organ follow Gaussian distributions, is invalid in prostate MR images. To this end, the authors propose a sparse dictionary learning method to model the image appearance in a nonparametric fashion and further integrate the appearance model into a deformable segmentation framework for prostate MR segmentation. Methods: To drive the deformable model for prostate segmentation, the authors propose nonparametric appearance and shape models. The nonparametric appearance model is based on a novel dictionary learning method, namely distributed discriminative dictionary (DDD) learning, which is able to capture fine distinctions in image appearance. To increase the differential power of traditional dictionary-based classification methods, the authors' DDD learning approach takes three strategies. First, two dictionaries for prostate and nonprostate tissues are built, respectively, using the discriminative features obtained from minimum redundancy maximum relevance feature selection. Second, linear discriminant analysis is employed as a linear classifier to boost the optimal separation between prostate and nonprostate tissues, based on the representation residuals from sparse representation. Third, to enhance the robustness of the authors' classification method, multiple local dictionaries are learned for local regions along the prostate boundary (each with small appearance variations), instead of learning one global classifier for the entire prostate. These discriminative dictionaries are located on different patches of the prostate surface and trained to adaptively capture the appearance in different prostate zones, thus achieving better local tissue differentiation. For each local region, multiple classifiers are trained based on the randomly selected samples and finally assembled by a specific fusion method. In addition to this nonparametric appearance model, a prostate shape model is learned from the shape statistics using a novel approach, sparse shape composition, which can model nonGaussian distributions of shape variation and regularize the 3D mesh deformation by constraining it within the observed shape subspace. Results: The proposed method has been evaluated on two datasets consisting of T2-weighted MR prostate images. For the first (internal) dataset, the classification effectiveness of the authors' improved dictionary learning has been validated by comparing it with three other variants of traditional dictionary learning methods. The experimental results show that the authors' method yields a Dice Ratio of 89.1% compared to the manual segmenta...

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Prostate Segmentation by Sparse Representation Based Classification

Cited by 21 publications

References 17 publications

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

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

LINKS: Learning-Based Multi-source IntegratioN FrameworK for Segmentation of Infant Brain Images

Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning

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