Breast Anatomy Enriched Tumor Saliency Estimation
| Autor: | Jianrui Ding, Yingtao Zhang, Boyu Zhang, Fei Xu, Chunping Ning, Heng-Da Cheng, Ying Wang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
ComputingMethodologies_SIMULATIONANDMODELING
Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 03 medical and health sciences 0302 clinical medicine Breast cancer 0202 electrical engineering electronic engineering information engineering medicine skin and connective tissue diseases Breast ultrasound Breast anatomy Artificial neural network medicine.diagnostic_test business.industry Breast structure Pattern recognition Image segmentation medicine.disease Tumor detection ComputingMethodologies_PATTERNRECOGNITION 030220 oncology & carcinogenesis Pattern recognition (psychology) 020201 artificial intelligence & image processing Artificial intelligence business |
| Zdroj: | ICPR |
| Popis: | Breast cancer investigation is of great significance, and developing tumor detection methodologies is a critical need. However, it is challenging for breast cancer detection using breast ultrasound (BUS) images due to the complicated breast structure and poor quality of the images. This paper proposes a novel tumor saliency estimation (TSE) model guided by enriched breast anatomy knowledge to localize the tumor. First, the breast anatomy layers are generated by a deep neural network. Then we refine the layers by integrating a non-semantic breast anatomy model to solve the problems of incomplete mammary layers. Meanwhile, a new background map generation method weighted by the semantic probability and spatial distance is proposed to improve the performance. The experiment demonstrates that the proposed method with the new background map outperforms four state-of-the-art TSE models with an increasing 10% of $F_{measure}$ on the public BUS dataset. |
| Databáze: | OpenAIRE |
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