Detecting and Localizing Prostate Cancer from Diffusion-Weighted Magnetic Resonance Imaging
| Autor: | Robert S. Keynton, Ahmed Shalaby, Ayman El-Baz, Ahmed Aboulfotouh, Mohamed Abou El-Ghar, Mohammed Ghazal, Mohammed Elmogy, Ashraf Khalil, Islam Reda, Moumen T. El-Melegy |
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| Rok vydání: | 2019 |
| Předmět: |
medicine.diagnostic_test
Computer science business.industry Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition Magnetic resonance imaging computer.software_genre medicine.disease Convolutional neural network 030218 nuclear medicine & medical imaging 03 medical and health sciences Prostate cancer 0302 clinical medicine medicine.anatomical_structure Voxel Feature (computer vision) Prostate 030220 oncology & carcinogenesis medicine Effective diffusion coefficient Artificial intelligence business computer |
| Zdroj: | ICIP |
| Popis: | The purpose of this work is to develop a computer-aided diagnosis (CAD) system for detecting and localizing prostate cancer from diffusion-weighted magnetic resonance imaging (DWI) acquired at five distinct b-values. The first step in the proposed system depends on nonnegative matrix factorization (NMF) to fuse intensity features of prostate voxels, spatial features of neighboring voxels, and shape prior features to guide the evolution of a level set function for accurate prostate segmentation. The second step in the proposed system involves calculating the apparent diffusion coefficient (ADC) maps of the segmented prostate regions as a discriminating feature between malignant and healthy cases. These ADC maps are used in the last step of the CAD system to train a convolutional neural network (CNN)-based model to identify the ADC maps with malignant tumors. To evaluate the accuracy of the system, 50% of the ADC maps are randomly chosen to train the CNN-model while the second 50% of the ADC maps are used to evaluate the accuracy of the trained model. The proposed CAD system resulted in an average area under the receiver operating characteristic curve (AUC) of 0.93 at the five b-values. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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