Abstract：
BACKGROUND AND OBJECTIVE:Prostate segmentation on Magnetic Resonance (MR) imaging is problematic because disease changes the shape and boundaries of the gland and it can be difficult to separate the prostate from surrounding tissues. We propose an automated model that extracts and combines multi-level features in a deep neural network to segment prostate on MR images. METHODS:Our proposed model, the Propagation Deep Neural Network (P-DNN), incorporates the optimal combination of multi-level feature extraction as a single model. High level features from the convolved data using DNN are extracted for prostate localization and shape recognition, while labeling propagation, by low level cues, is embedded into a deep layer to delineate the prostate boundary. RESULTS:A well-recognized benchmarking dataset (50 training data and 30 testing data from patients) was used to evaluate the P-DNN. When compared it to existing DNN methods, the P-DNN statistically outperformed the baseline DNN models with an average improvement in the DSC of 3.19%. When compared to the state-of-the-art non-DNN prostate segmentation methods, P-DNN was competitive by achieving 89.9 ± 2.8% DSC and 6.84 ± 2.5 mm HD on training sets and 84.13 ± 5.18% DSC and 9.74 ± 4.21 mm HD on testing sets. CONCLUSION:Our results show that P-DNN maximizes multi-level feature extraction for prostate segmentation of MR images.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Yan K,Wang X,Kim J,Khadra M,Fulham M,Feng Ddoi
10.1016/j.cmpb.2018.12.031subject
Has Abstractpub_date
2019-03-01 00:00:00pages
11-21eissn
0169-2607issn
1872-7565pii
S0169-2607(18)31099-Xjournal_volume
170pub_type
杂志文章abstract：:As health care costs have risen dramatically, the use of clinical data to analyze the quality of health care provided has increased. Central to this analysis is the means by which the clinical data itself is obtained. The reliability and validity of the data must be established in order to insure credible use of the d...
journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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journal_title：Computer methods and programs in biomedicine
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