CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images
Medical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8739 |
| _version_ | 1827765438703992832 |
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| author | Ruifen Cao Long Ning Chao Zhou Pijing Wei Yun Ding Dayu Tan Chunhou Zheng |
| author_facet | Ruifen Cao Long Ning Chao Zhou Pijing Wei Yun Ding Dayu Tan Chunhou Zheng |
| author_sort | Ruifen Cao |
| collection | DOAJ |
| description | Medical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy for small objects is still unsatisfactory. Current segmentation methods based on deep learning find it difficult to extract multiple scale features of medical images, leading to an insufficient detection capability for smaller objects. In this paper, we propose a context feature fusion and attention mechanism based network for small target segmentation in medical images called CFANet. CFANet is based on U-Net structure, including the encoder and the decoder, and incorporates two key modules, context feature fusion (CFF) and effective channel spatial attention (ECSA), in order to improve segmentation performance. The CFF module utilizes contextual information from different scales to enhance the representation of small targets. By fusing multi-scale features, the network captures local and global contextual cues, which are critical for accurate segmentation. The ECSA module further enhances the network’s ability to capture long-range dependencies by incorporating attention mechanisms at the spatial and channel levels, which allows the network to focus on information-rich regions while suppressing irrelevant or noisy features. Extensive experiments are conducted on four challenging medical image datasets, namely ADAM, LUNA16, Thoracic OAR, and WORD. Experimental results show that CFANet outperforms state-of-the-art methods in terms of segmentation accuracy and robustness. The proposed method achieves excellent performance in segmenting small targets in medical images, demonstrating its potential in various clinical applications. |
| first_indexed | 2024-03-11T11:21:55Z |
| format | Article |
| id | doaj.art-fb918663e2224d40983456478ed87d74 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:55Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-fb918663e2224d40983456478ed87d742023-11-10T15:11:52ZengMDPI AGSensors1424-82202023-10-012321873910.3390/s23218739CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical ImagesRuifen Cao0Long Ning1Chao Zhou2Pijing Wei3Yun Ding4Dayu Tan5Chunhou Zheng6Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, ChinaInformation Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, ChinaInstitute of Energy, Hefei Comprehensive National Science Center, Hefei 230031, ChinaInstitutes of Physical Science and Information Technology, Anhui University, Hefei 230601, ChinaKey Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, ChinaInstitutes of Physical Science and Information Technology, Anhui University, Hefei 230601, ChinaKey Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Artificial Intelligence, Anhui University, Hefei 230601, ChinaMedical image segmentation plays a crucial role in clinical diagnosis, treatment planning, and disease monitoring. The automatic segmentation method based on deep learning has developed rapidly, with segmentation results comparable to clinical experts for large objects, but the segmentation accuracy for small objects is still unsatisfactory. Current segmentation methods based on deep learning find it difficult to extract multiple scale features of medical images, leading to an insufficient detection capability for smaller objects. In this paper, we propose a context feature fusion and attention mechanism based network for small target segmentation in medical images called CFANet. CFANet is based on U-Net structure, including the encoder and the decoder, and incorporates two key modules, context feature fusion (CFF) and effective channel spatial attention (ECSA), in order to improve segmentation performance. The CFF module utilizes contextual information from different scales to enhance the representation of small targets. By fusing multi-scale features, the network captures local and global contextual cues, which are critical for accurate segmentation. The ECSA module further enhances the network’s ability to capture long-range dependencies by incorporating attention mechanisms at the spatial and channel levels, which allows the network to focus on information-rich regions while suppressing irrelevant or noisy features. Extensive experiments are conducted on four challenging medical image datasets, namely ADAM, LUNA16, Thoracic OAR, and WORD. Experimental results show that CFANet outperforms state-of-the-art methods in terms of segmentation accuracy and robustness. The proposed method achieves excellent performance in segmenting small targets in medical images, demonstrating its potential in various clinical applications.https://www.mdpi.com/1424-8220/23/21/8739medical image segmentationconvolution neural networkcontext feature fusionattention mechanism |
| spellingShingle | Ruifen Cao Long Ning Chao Zhou Pijing Wei Yun Ding Dayu Tan Chunhou Zheng CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images Sensors medical image segmentation convolution neural network context feature fusion attention mechanism |
| title | CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images |
| title_full | CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images |
| title_fullStr | CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images |
| title_full_unstemmed | CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images |
| title_short | CFANet: Context Feature Fusion and Attention Mechanism Based Network for Small Target Segmentation in Medical Images |
| title_sort | cfanet context feature fusion and attention mechanism based network for small target segmentation in medical images |
| topic | medical image segmentation convolution neural network context feature fusion attention mechanism |
| url | https://www.mdpi.com/1424-8220/23/21/8739 |
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