Efficient automated detection of mitotic cells from breast histological images using deep convolution neutral network with wavelet decomposed patches
| Autor: | Pranab K. Dutta, Dev Kumar Das |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Computer science Mitosis Health Informatics Breast Neoplasms Convolutional neural network Convolution 03 medical and health sciences 0302 clinical medicine Wavelet Image Processing Computer-Assisted Humans Neutral network Pixel business.industry Medical practice Pattern recognition Haar wavelet Computer Science Applications 030104 developmental biology Female Artificial intelligence Neural Networks Computer business 030217 neurology & neurosurgery Algorithms |
| Zdroj: | Computers in biology and medicine. 104 |
| ISSN: | 1879-0534 |
| Popis: | In medical practice, the mitotic cell count from histological images acts as a proliferative marker for cancer diagnosis. Therefore, an accurate method for detecting mitotic cells in histological images is essential for cancer screening. Manual evaluation of clinically relevant image features that might reflect mitotic cells in histological images is time-consuming and error prone, due to the heterogeneous physical characteristics of mitotic cells. Computer-assisted automated detection of mitotic cells could overcome these limitations of manual analysis and act as a useful tool for pathologists to make cancer diagnoses efficiently and accurately. Here, we propose a new approach for mitotic cell detection in breast histological images that uses a deep convolution neural network (CNN) with wavelet decomposed image patches. In this approach, raw image patches of 81 × 81 pixels are decomposed to patches of 21 × 21 pixels using Haar wavelet and subsequently used in developing a deep CNN model for automated detection of mitotic cells. The decomposition step reduces convolution time for mitotic cell detection relative to the use of raw image patches in conventional CNN models. The proposed deep network was tested using the MITOS (ICPR2012) and MITOS-ATYPIA-14 breast cancer histological datasets and shown to outperform existing algorithms for mitotic cell detection. Overall, our method improves the performance and reduces the computational burden of conventional deep CNN approaches for mitotic cell detection. |
| Databáze: | OpenAIRE |
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