Mutual Information Loss in Pyramidal Image Processing

Gibson, J.D.; Oh, Hyemin

doi:10.3390/info11060322

Cited by 9 publications

(8 citation statements)

References 18 publications

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“…This process is also similar to the strategy used by human vision system 40 . It has been successfully used for image processing, such as segmentation and registration 29,30,33 . However, the performance of this paradigm has seldomly been explored in the image generation tasks.…”

Section: Methodsmentioning

confidence: 99%

“…To produce multiresolution image data required by our proposed model, downsampling and upsampling strategies shown in Figure A2 were used 29 . Downsampling images by a factor of 2 should have produced four images.…”

Section: Methodsmentioning

confidence: 99%

“…40 It has been successfully used for image processing, such as segmentation and registration. 29,30,33 However, the performance of this paradigm has seldomly been explored in the image generation tasks. In this study, we incorporate our proposed RDNN into this paradigm to formulate our proposed multiresolution RDNN model, as shown in Figure 3.…”

Section: Network Architecturementioning

confidence: 99%

“…The multiresolution strategy has been successfully used in many computer vision and medical image processing tasks, such as image segmentation and optimization. [29][30][31] The key idea behind this strategy is that optimization should be conducted progressively from low to high resolution, not at a single resolution level. This optimization strategy can resolve major numerical instability problems, such as local minima.…”

Section: Introductionmentioning

confidence: 99%

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Multiresolution residual deep neural network for improving pelvic CBCT image quality

Cai

et al. 2022

Medical Physics

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Cone-beam computed tomography (CBCT) is frequently used for accurate image-guided radiation therapy. However, the poor CBCT image quality prevents its further clinical use. Thus, it is important to improve the HU accuracy and structure preservation of CBCT images. Methods: In this study, we proposed a novel method to generate synthetic CT (sCT) images from CBCT images. A multiresolution residual deep neural network (RDNN) was adopted for image regression from CBCT images to planning CT (pCT) images. At the coarse level, RDNN was first trained with a large amount of lower resolution images, which can make the network focus on coarse information and prevent overfitting problems. More fine information was obtained gradually by fine-tuning the coarse model using fewer number of higher resolution images. Our model was optimized by using aligned pCT and CBCT image pairs of a particular body region of 153 prostate cancer patients treated in our hospital (120 for training and 33 for testing). Five-fold cross-validation was used to tune the hyperparameters and the testing data were used to evaluate the performance of the final models. Results:The mean absolute error (MAE) between CBCT and pCT on the testing data was 352.56 HU, while the MAE between the sCT and pCT images was 52.18 HU for our proposed multiresolution RDNN model, which reduced the MAE by 85.20% (p < 0.01). In addition, the average structural similarity index measure between the sCT and CBCT was 19.64% (p = 0.01) higher than that of pCT and CBCT. Conclusions: The sCT images generated using our proposed multiresolution RDNN have higher HU accuracy and structural fidelity, which may promote the further applications of CBCT images in the clinic for structure segmentation, dose calculation, and adaptive radiotherapy planning.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Network Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multiresolution residual deep neural network for improving pelvic CBCT image quality

Cai

et al. 2022

Medical Physics

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“…Lately, deep learning has been proved as an efficient tool for signal and image processing [10][11][12]. The revival of deep learning methods, especially, convolutional neural networks (CNNs), provides a new perspective for FBP problem.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Facial Beauty Prediction

et al. 2020

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Facial beauty prediction (FBP) is a burgeoning issue for attractiveness evaluation, which aims to make assessment consistent with human opinion. Since FBP is a regression problem, to handle this issue, there are data-driven methods for finding the relations between facial features and beauty assessment. Recently, deep learning methods have shown its amazing capacity for feature representation and analysis. Convolutional neural networks (CNNs) have shown tremendous performance on facial recognition and comprehension, which are proved as an effective method for facial feature exploration. Lately, there are well-designed networks with efficient structures investigated for better representation performance. However, these designs concentrate on the effective block but do not build an efficient information transmission pathway, which led to a sub-optimal capacity for feature representation. Furthermore, these works cannot find the inherent correlations of feature maps, which also limits the performance. In this paper, an elaborate network design for FBP issue is proposed for better performance. A residual-in-residual (RIR) structure is introduced to the network for passing the gradient flow deeper, and building a better pathway for information transmission. By applying the RIR structure, a deeper network can be established for better feature representation. Besides the RIR network design, an attention mechanism is introduced to exploit the inner correlations among features. We investigate a joint spatial-wise and channel-wise attention (SCA) block to distribute the importance among features, which finds a better representation for facial information. Experimental results show our proposed network can predict facial beauty closer to a human’s assessment than state-of-the-arts.

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