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This is especially true with modern processes utilizing zirconia dust, investments dust, alumina dust, glass beads, monomer fumes and gypsum dust. We support you with our experience Dust exposure can be dramatically reduced using optimized funnel systems and state-of the-art suction equipment. On This Page. Name Poster. How to Pronounce Bgfe. Is this an accurate pronunciation? How difficult is it to pronounce Bgfe? Can Bgfe be pronounced multiple ways?
Record your pronunciation Recording. Click to stop. We noticed you have a microphone. If you know how to pronounce Bgfe, just click the button to record. We'll save it, review it, and post it to help others. Recordings from children under 18 are not allowed. Back to Top. Meaning and Origin What does the name Bgfe mean? Origin and Meaning of Bgfe. TABLE 2. Measurement results of different segmentation methods on the BUSI dataset. Figure 4 visually compares the segmentation results obtained by our approach and the other five segmentation methods.
As shown in the figure, our approach precisely segments the breast lesion regions from ultrasound images despite of sevious artifacts, while the other methods tend to generate over or under-segmentation results as they wrongly classify some non-lesion regions or miss parts of lesion regions. In the first and second rows where high speckle noise is presented, our result shows the highest similarity against the ground true.
This is because the boundary detection loss in our loss function explicitly regularizes the boundary shape of the detected regions using the boundary information in the ground true. In addition, non-lesion regions are greatly removed even though there are ambiguous regions with weak boundaries, see the third and fourth rows, since the multiscale shceme in our approach effectively fuses the information from different image scales.
Moreover, our approach accurately locate the boundaries of breast lesion regions in inhomogeneous ultrasound images attributing to the boundary feature enhancement of the BGFE module, see the fifth and sixth rows.
In contrast, segmentation results from the other methods are inferior as these methods have limited capability to cope with strong ultrasound artifacts.
Comparison of breast lesion segmentation among different methods. A Testing images. B Ground truth denoted as GT.
We conduct an ablation study to evaluate the key components of the proposed approach. Specifically, three baseline networks are considered and their quantitative results on the two datasets are reported in comparison with our approach. Tables 3 , 4 present the measurement results of different baseline networks on the two datasets, respectively. This clearly demonstrates the benifits from the FPN module and the multiscale scheme.
In addition, our approach achieves the best result compared with the three baseline networks, which validates the superiority of the proposed approach by combining boundary feature enhancement and multiscale fusing into a unified framework. TABLE 3. TABLE 4. Measurement results of different baseline networks on the BUSI dataset.
Figure 5 visually compares the segmentation results obtained by our approach and the three baseline networks. Apparently, our approach better segments breast lesion regions than the three baseline networks.
In contrast, our approach accurately locates the boundaries of breast lesion regions by learning an enhanced boundary map using the BGFE module. Moreover, false detections are effectively removed attributing to the multiscale scheme. Thus, our result achieves the highest similarity against the ground true. Comparison of breast lesion segmentation between our approach C and the three baseline networks D—F against the ground truth B.
This paper proposes a novel boundary-guided multiscale network to boost the performance of breast lesion segmentation from ultrasound images based on the FPN framework.
By combining boundary feature enhancement and multiscale image information into a unified framework, the boundary detection capability of the FPN framework is greatly improved so that weak boundaries in ambiguous regions can be correctly identified. In addition, the segmentation accuracy is notably increased as false detections resulted from strong ultrasound artifacts are effectively removed attributing to the multiscale scheme.
Experimental results on two challenging breast ultrasound datasets demonstrate the superiority of our approach compared with state-of-the-art methods.
However, similar to previous work, our approach also relies on labeled data to train the network, which limits its applications in scenarios where unlabeled data is presented. Thus, the future work will consider the adaptation from labeled data to unlabeled data in order to improve the generalization of the proposed approach.
The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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