Abstract:Accurately segmented nuclei are important, not only for cancer classification, but also for predicting treatment effectiveness and other biomedical applications. However, the diversity of cell types, various external factors, and illumination conditions make nucleus segmentation a challenging task. In this work, we present a new deep learning-based method for cell nucleus segmentation. The proposed convolutional blur attention (CBA) network consists of downsampling and upsampling procedures. A blur attention module and a blur pooling operation are used to retain the feature salience and avoid noise generation in the downsampling procedure. A pyramid blur pooling (PBP) module is proposed to capture the multi-scale information in the upsampling procedure. The superiority of the proposed method has been compared with a few prior segmentation models, namely U-Net, ENet, SegNet, LinkNet, and Mask RCNN on the 2018 Data Science Bowl (DSB) challenge dataset and the multi-organ nucleus segmentation (MoNuSeg) at MICCAI 2018. The Dice similarity coefficient and some evaluation matrices, such as F1 score, recall, precision, and average Jaccard index (AJI) were used to evaluate the segmentation efficiency of these models. Overall, the proposal method in this paper has the best performance, the AJI indicator on the DSB dataset and MoNuSeg is 0.8429, 0.7985, respectively.Keywords: cell nuclei; convolutional neural network; deep learning; nucleus segmentation
The radius of the blur, specified as a . It defines the value of the standard deviation to the Gaussian function, i.e., how many pixels on the screen blend into each other; thus, a larger value will create more blur. A value of 0 leaves the input unchanged. The initial value for interpolation is 0.
Proper:Replacing contents via AJAX in SPA is same as moving page in traditional web page. In other words, the context of page is changed, therefore, the navigation links SHOULD be blurred when they are clicked.
ImageFiltered is the perfect widget for that . It creates a widget that applies an ImageFilter to its child.ImageFilter is an easy way to blur or transform pixels in your app . You can import it from dart:uiCode :
With Spin Blur, you can not only drag the four outer handles (top, bottom, left and right) to change the shape from a circle to an ellipse, you can also rotate the whole blur shape so that it operates at an angle. This gives it tremendous flexibility:
The other really neat trick is that you can move the center of the blur to anywhere you like. Hold the alt/option key, and drag it to its new location. Here, offsetting it to the middle of the hubcap produces a much better blur:
One of the best things about the Blur Gallery is that you can apply multiple blurs to the same image, without leaving the dialog. Clicking on the rear wheel produces a second Spin Blur effect, and you can change its shape and spin amount in exactly the same way to produce a matching blur on both wheels:
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Defocus blur detection (DBD) aims to separate blurred and unblurred regions for a given image. Due to its potential and practical applications, this task has attracted much attention. Most of the existing DBD models have achieved competitive performance by aggregating multi-level features extracted from fully convolutional networks. However, they also suffer from several challenges, such as coarse object boundaries of the defocus blur regions, background clutter, and the detection of low contrast focal regions. In this paper, we develop a hierarchical edge-aware network to solve the above problems, to the best of our knowledge, it is the first trial to develop an end-to-end network with edge awareness for DBD. We design an edge feature extraction network to capture boundary information, a hierarchical interior perception network is used to generate local and global context information, which is helpful to detect the low contrast focal regions. Moreover, a hierarchical edge-aware fusion network is proposed to hierarchically fuse edge information and semantic features. Benefiting from the rich edge information, the fused features can generate more accurate boundaries. Finally, we propose a progressive feature refinement network to refine the output features. Experimental results on two widely used DBD datasets demonstrate that the proposed model outperforms the state-of-the-art approaches.
In the past decade, many defocus blur detection methods have been proposed. These methods can be simply divided into two categories: traditional methods and deep learning based methods. The former one is based on hand-crafted features and utilizes low-level cues to predict DBD maps, such as frequency [5,6,7,8,9] and gradient [10,11,12,13,14,15]. However, these traditional methods can not well obtain global information of high-level semantic features; thus they can not accurately detect the low contrast focal regions (see green box region of Fig. 1a) and suppress the background clutter (see red box region of Fig. 1b). Otherwise, as shown in the blue box region of Fig. 1a, the boundaries of in-focus objects have not well been detected.
Recently, convolutional neural networks (CNNs) have been widely used in various computer vision tasks because of its powerful extraction capabilities, such as image denoising , image classification , super-resolution , salient object detection , and object tracking . Similarly, CNNs have also been well applied in DBD [16, 17, 24,25,26,27,28,29,30,31,32,33,34,35]. Although deep learning based approaches achieve higher performance and significant improvements compared with the traditional methods, there remain several problems that need to be further addressed: (1) the complementary of local and global information generated by different layers can not be well utilized, which causes ambiguous detection of low-contrast regions and background clutter of the final DBD map; (2) the boundaries of in-focus objects can not be fully distinguished.
In this paper, we exploit a hierarchical edge-aware network (HEANet) to improve above-mentioned problems, which consists of four sub-networks: hierarchical interior perception network (HIPNet), edge feature extraction network (EFENet), hierarchical edge-aware fusion network (HEFNet), progressive feature refinement network (PFRNet). Specifically, considering the contextual information can benefit for detecting low contrast focal regions, we design a receptive field context module (RFCM) to capture multi-receptive field features. In addition, we cascade three RFCMs and form a top-bottom manner as the HIPNet. Then, we develop an EFENet to obtain the edge information of in-focus objects from feature maps. Subsequently, the multi-scale contextual features and the edge information are transmitted to the HEFNet, which consists of some progressive edge guidance aggregation modules (EGAMs). With this module, the edge cues and multi-scale semantic features can be hierarchically fused, making better performance on localization. Finally, we design a PFRNet to refine the feature maps to generate a DBD map with clear region boundaries, and supervise the predictive DBD map with the ground truth.
The architecture of our HEANet. EFENet represents the edge feature extraction network. HIPNet is the hierarchical interior perception network. HEFNet represents the hierarchical edge-aware fusion network. PFRNet is the progressive feature refinement network
In the past years, many DBD methods have been proposed. Traditional methods based on the hand-crafted features, such as frequency [5,6,7,8,9], gradient [10,11,12,13,14,15], and so on [18, 36, 37]. Shi et al.  propose a few local blur features, such as image gradient, Fourier domain, and data-driven local filters, to enhance the capabilities of defocus blur detection. Pang et al.  develop a new kernel-specific feature vector for DBD, which incorporates the multiplication of the variance of filtered kernel and the variance of filtered patch gradients. Yi et al.  present a sharpness metric based on local binary patterns to distinguish defocus regions. Tang et al.  design a blur metric based on the log averaged spectrum residual to obtain a coarse blur map, then an iterative updating mechanism is used to refine the blur map. Golestaneh et al.  propose a novel method based on high-frequency multi-scale fusion and sort transform of gradient magnitudes to compute blur detection maps. These traditional methods can be effective in some cases; however, they are the limited capacity to obtain high-level semantic information in complex scenarios.
The framework of our method is illustrated in Fig. 2. Our approach includes four sub-networks: hierarchical interior perception network (HIPNet) which captures multi-scale contextual information, edge feature extraction network (EFENet) which extracts edge information, hierarchical edge-aware fusion network (HEFNet) which guides the extracted features hierarchical fusion by taking advantage of the edge information of low-level features, finally, progressive feature refinement network (PFRNet) is used to fuse and refine features progressively to generate the defocus blur map. These sub-networks consist of different modules. The details are introduced as follows. 781b155fdc
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