کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4964846 | 1447939 | 2017 | 31 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A comprehensive non-invasive framework for diagnosing prostate cancer
ترجمه فارسی عنوان
چارچوب جامع غیر تهاجمی برای تشخیص سرطان پروستات
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Early detection of prostate cancer increases chances of patients' survival. Our automated non-invasive system for computer-aided diagnosis (CAD) of prostate cancer segments the prostate on diffusion-weighted magnetic resonance images (DW-MRI) acquired at different b-values, estimates its apparent diffusion coefficients (ADC), and classifies their descriptors - empirical cumulative distribution functions (CDF) - with a trained deep learning network. To segment the prostate, an evolving geometric (level-set-based) deformable model is guided by a speed function depending on intensity attributes extracted from the DW-MRI with nonnegative matrix factorization (NMF). For a more robust evolution, the attributes are fused with a probabilistic shape prior and estimated spatial dependencies between prostate voxels. To preserve continuity, the ADCs of the segmented prostate volume at different b-values are normalized and refined using a generalized Gauss-Markov random field image model. The CDFs of the refined ADCs at different b-values are considered global water diffusion features and used to distinguish between benign and malignant prostates. A deep learning network of stacked non-negativity-constrained auto-encoders (SNCAE) is trained to classify the benign or malignant prostates on the basis of the constructed CDFs. Our experiments on 53 clinical DW-MRI data sets resulted in 92.3% accuracy, 83.3% sensitivity, and 100% specificity, indicating that the proposed CAD system could be used as a reliable non-invasive diagnostic tool.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Biology and Medicine - Volume 81, 1 February 2017, Pages 148-158
Journal: Computers in Biology and Medicine - Volume 81, 1 February 2017, Pages 148-158
نویسندگان
Islam Reda, Ahmed Shalaby, Mohammed Elmogy, Ahmed Abou Elfotouh, Fahmi Khalifa, Mohamed Abou El-Ghar, Ehsan Hosseini-Asl, Georgy Gimel'farb, Naoufel Werghi, Ayman El-Baz,