Fully Convolutional DenseNet with Multiscale Context for Automated Breast Tumor Segmentation
Autor: | Hongna Tan, Kai Qiao, Dapeng Shi, Jingbo Xu, Bin Yan, Jian Chen, Lei Zeng, Jinjin Hai |
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Rok vydání: | 2019 |
Předmět: |
lcsh:Medical technology
Article Subject Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Biomedical Engineering Breast Neoplasms Health Informatics Context (language use) 02 engineering and technology Overfitting 030218 nuclear medicine & medical imaging Convolution 03 medical and health sciences 0302 clinical medicine Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Preprocessor Segmentation Breast lcsh:R5-920 Network architecture Pixel Artificial neural network business.industry Pattern recognition ComputingMethodologies_PATTERNRECOGNITION lcsh:R855-855.5 Female 020201 artificial intelligence & image processing Surgery Neural Networks Computer Artificial intelligence lcsh:Medicine (General) business Algorithms Research Article Mammography Biotechnology |
Zdroj: | Journal of Healthcare Engineering, Vol 2019 (2019) Journal of Healthcare Engineering |
ISSN: | 2040-2309 2040-2295 |
Popis: | Breast tumor segmentation plays a crucial role in subsequent disease diagnosis, and most algorithms need interactive prior to firstly locate tumors and perform segmentation based on tumor-centric candidates. In this paper, we propose a fully convolutional network to achieve automatic segmentation of breast tumor in an end-to-end manner. Considering the diversity of shape and size for malignant tumors in the digital mammograms, we introduce multiscale image information into the fully convolutional dense network architecture to improve the segmentation precision. Multiple sampling rates of atrous convolution are concatenated to acquire different field-of-views of image features without adding additional number of parameters to avoid over fitting. Weighted loss function is also employed during training according to the proportion of the tumor pixels in the entire image, in order to weaken unbalanced classes problem. Qualitative and quantitative comparisons demonstrate that the proposed algorithm can achieve automatic tumor segmentation and has high segmentation precision for various size and shapes of tumor images without preprocessing and postprocessing. |
Databáze: | OpenAIRE |
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