111 research outputs found
Monte Carlo-based Noise Compensation in Coil Intensity Corrected Endorectal MRI
Background: Prostate cancer is one of the most common forms of cancer found
in males making early diagnosis important. Magnetic resonance imaging (MRI) has
been useful in visualizing and localizing tumor candidates and with the use of
endorectal coils (ERC), the signal-to-noise ratio (SNR) can be improved. The
coils introduce intensity inhomogeneities and the surface coil intensity
correction built into MRI scanners is used to reduce these inhomogeneities.
However, the correction typically performed at the MRI scanner level leads to
noise amplification and noise level variations. Methods: In this study, we
introduce a new Monte Carlo-based noise compensation approach for coil
intensity corrected endorectal MRI which allows for effective noise
compensation and preservation of details within the prostate. The approach
accounts for the ERC SNR profile via a spatially-adaptive noise model for
correcting non-stationary noise variations. Such a method is useful
particularly for improving the image quality of coil intensity corrected
endorectal MRI data performed at the MRI scanner level and when the original
raw data is not available. Results: SNR and contrast-to-noise ratio (CNR)
analysis in patient experiments demonstrate an average improvement of 11.7 dB
and 11.2 dB respectively over uncorrected endorectal MRI, and provides strong
performance when compared to existing approaches. Conclusions: A new noise
compensation method was developed for the purpose of improving the quality of
coil intensity corrected endorectal MRI data performed at the MRI scanner
level. We illustrate that promising noise compensation performance can be
achieved for the proposed approach, which is particularly important for
processing coil intensity corrected endorectal MRI data performed at the MRI
scanner level and when the original raw data is not available.Comment: 23 page
Estimating Optimal Depth of VGG Net with Tree-Structured Parzen Estimators
Deep convolutional neural networks (CNNs) have shown astonishingperformances in variety of fields. However, different architecturesof the networks are required for different datasets, and findingright architecture for given data has been a topic of great interest incomputer vision communities. One of the most important factors ofthe CNNs architecture is the depth of the networks, which plays asignificant role in avoiding over-fitting. Grid Search is widely usedfor estimating the depth, but it requires huge computation time. Motivatedby this, a method for finding an optimal architecture depth isintroduced, which is based on a hyper-parameter optimizer calledTree-Structured Parzen Estimators (TPE). In this work, we showthat the TPE is capable of estimating the CNNs architecture depthwith an accuracy of 83.33% with CIFAR-10 dataset and 60.00%with CIFAR-100 dataset while it reduces the computation time bymore 70% compared to the Grid Search
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