scholarly journals A Semantic Segmentation Algorithm Using FCN with Combination of BSLIC

2018 ◽  
Vol 8 (4) ◽  
pp. 500 ◽  
Author(s):  
Wei Zhao ◽  
Haodi Zhang ◽  
Yujin Yan ◽  
Yi Fu ◽  
Hai Wang
Keyword(s):  
Semantic Segmentation ◽  
Segmentation Algorithm

scholarly journals Semantic Image Segmentation with Deep Convolutional Neural Networks and Quick Shift

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 427 ◽  
Author(s):  
Sanxing Zhang ◽  
Zhenhuan Ma ◽  
Gang Zhang ◽  
Tao Lei ◽  
Rui Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Input Image ◽  
Feature Representation ◽  
Segmentation Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Semantic Image Segmentation

Semantic image segmentation, as one of the most popular tasks in computer vision, has been widely used in autonomous driving, robotics and other fields. Currently, deep convolutional neural networks (DCNNs) are driving major advances in semantic segmentation due to their powerful feature representation. However, DCNNs extract high-level feature representations by strided convolution, which makes it impossible to segment foreground objects precisely, especially when locating object boundaries. This paper presents a novel semantic segmentation algorithm with DeepLab v3+ and super-pixel segmentation algorithm-quick shift. DeepLab v3+ is employed to generate a class-indexed score map for the input image. Quick shift is applied to segment the input image into superpixels. Outputs of them are then fed into a class voting module to refine the semantic segmentation results. Extensive experiments on proposed semantic image segmentation are performed over PASCAL VOC 2012 dataset, and results that the proposed method can provide a more efficient solution.

Real-Time Semantic Segmentation Algorithm Based on Feature Fusion Technology

2020 ◽  
Vol 57 (2) ◽  
pp. 021011
Author(s):  
蔡雨 Cai Yu ◽  
黄学功 Huang Xuegong ◽  
张志安 Zhang Zhian ◽  
朱新年 Zhu Xinnian ◽  
马祥 Ma Xiang
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Segmentation Algorithm ◽  
Fusion Technology

Segmentation of acetowhite region in uterine cervical image based on deep learning

2021 ◽  
pp. 1-14
Author(s):  
Jun Liu ◽  
Tong Liang ◽  
Yun Peng ◽  
Gengyou Peng ◽  
Lechan Sun ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Deep Learning ◽  
Clustering Algorithm ◽  
Precancerous Lesions ◽  
Semantic Segmentation ◽  
Segmentation Algorithm ◽  
Cervical Region ◽  
Automatic Methods ◽  
Traditional Approaches ◽  
Mean Sensitivity

BACKGROUND: Acetowhite (AW) region is a critical physiological phenomenon of precancerous lesions of cervical cancer. An accurate segmentation of the AW region can provide a useful diagnostic tool for gynecologic oncologists in screening cervical cancers. Traditional approaches for the segmentation of AW regions relied heavily on manual or semi-automatic methods. OBJECTIVE: To automatically segment the AW regions from colposcope images. METHODS: First, the cervical region was extracted from the original colposcope images by k-means clustering algorithm. Second, a deep learning-based image semantic segmentation model named DeepLab V3+ was used to segment the AW region from the cervical image. RESULTS: The results showed that, compared to the fuzzy clustering segmentation algorithm and the level set segmentation algorithm, the new method proposed in this study achieved a mean Jaccard Index (JI) accuracy of 63.6% (improved by 27.9% and 27.5% respectively), a mean specificity of 94.9% (improved by 55.8% and 32.3% respectively) and a mean accuracy of 91.2% (improved by 38.6% and 26.4% respectively). A mean sensitivity of 78.2% was achieved by the proposed method, which was 17.4% and 10.1% lower respectively. Compared to the image semantic segmentation models U-Net and PSPNet, the proposed method yielded a higher mean JI accuracy, mean sensitivity and mean accuracy. CONCLUSION: The improved segmentation performance suggested that the proposed method may serve as a useful complimentary tool in screening cervical cancer.

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