Breast cancer mitotic cell detection using cascade convolutional neural network with U-Net
| Autor: | Miaomiao Sun, Xi Lu, Zhihong Zhang, Zejun You, Jing Wu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
lcsh:Biotechnology Mitosis Breast Neoplasms 02 engineering and technology Convolutional neural network Set (abstract data type) breast cancer lcsh:TP248.13-248.65 0502 economics and business 0202 electrical engineering electronic engineering information engineering Image Processing Computer-Assisted Humans Segmentation Block (data storage) binary classification business.industry Applied Mathematics Deep learning cascade detection lcsh:Mathematics 05 social sciences deep learning Pattern recognition General Medicine lcsh:QA1-939 semantic segmentation Data set Computational Mathematics Binary classification Modeling and Simulation mitosis automatic detection 020201 artificial intelligence & image processing Female Artificial intelligence Neural Networks Computer General Agricultural and Biological Sciences business 050203 business & management Algorithms |
| Zdroj: | Mathematical Biosciences and Engineering, Vol 18, Iss 1, Pp 673-695 (2021) |
| ISSN: | 1551-0018 |
| DOI: | 10.3934/mbe.2021036?viewType=HTML |
| Popis: | The number of mitotic tumor cells detected in each slide is one of the key indicators of breast cancer prognosis. However, accurate mitotic cell counts are still a difficult problem for pathologists and related experts. Traditional methods use manual design algorithms to extract features of mitotic cells, and most methods rely on sliding windows to achieve pixel-level classification through deep learning. However, the complex background and high resolution of pathological images make the above methods time-consuming and ineffective. In order to solve the above problems, we propose a new cascaded convolutional neural network UBCNN (cascaded CNN based on UNet), which consists of three parts: semantic segmentation and classification to detect mitosis. First, we use an improved UNet ++ segmentation network to locate the candidate set of mitotic targets. Secondly, an adequately labeled cell nucleus data set is sent to an improved two-dimensional VNet network, and the cell nucleus is located by means of semantic segmentation to obtain accurate image blocks of mitotic and non-mitotic cells. Finally, the obtained cell image block is used to train a convolutional neural network to achieve binary classification, and the candidate set area is screened to retain the final result of mitosis cells. This paper verifies the detection effect of the above-mentioned cascade detection algorithm on the ICPR 2012 and 2014 mitosis automatic detection competition data sets. The evaluation indicators include accuracy, recall and F-score. Our cascade detection algorithm based on segmentation and classification reached 0.831 on the ICPR 2012 data set and 0.576 on the ICPR 2014 data set. Compared with other existing algorithms, the detection effect was improved, which was very competitive. |
| Databáze: | OpenAIRE |
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