AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images
The acurate segmentation and classification of nuclei in histological images are crucial for the diagnosis and treatment of colorectal cancer. However, the aggregation of nuclei and intra-class variability in histology images present significant challenges for nuclei segmentation and classification....
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/22/7208 |
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| author | Ruifen Cao Qingbin Meng Dayu Tan Pijing Wei Yun Ding Chunhou Zheng |
| author_facet | Ruifen Cao Qingbin Meng Dayu Tan Pijing Wei Yun Ding Chunhou Zheng |
| author_sort | Ruifen Cao |
| collection | DOAJ |
| description | The acurate segmentation and classification of nuclei in histological images are crucial for the diagnosis and treatment of colorectal cancer. However, the aggregation of nuclei and intra-class variability in histology images present significant challenges for nuclei segmentation and classification. In addition, the imbalance of various nuclei classes exacerbates the difficulty of nuclei classification and segmentation using deep learning models. To address these challenges, we present a novel attention-enhanced residual refinement network (AER-Net), which consists of one encoder and three decoder branches that have same network structure. In addition to the nuclei instance segmentation branch and nuclei classification branch, one branch is used to predict the vertical and horizontal distance from each pixel to its nuclear center, which is combined with output by the segmentation branch to improve the final segmentation results. The AER-Net utilizes an attention-enhanced encoder module to focus on more valuable features. To further refine predictions and achieve more accurate results, an attention-enhancing residual refinement module is employed at the end of each encoder branch. Moreover, the coarse predictions and refined predictions are combined by using a loss function that employs cross-entropy loss and generalized dice loss to efficiently tackle the challenge of class imbalance among nuclei in histology images. Compared with other state-of-the-art methods on two colorectal cancer datasets and a pan-cancer dataset, AER-Net demonstrates outstanding performance, validating its effectiveness in nuclear segmentation and classification. |
| format | Article |
| id | doaj-art-26d9080d232242eb9729fab76e04efb0 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-26d9080d232242eb9729fab76e04efb02025-08-20T01:53:56ZengMDPI AGSensors1424-82202024-11-012422720810.3390/s24227208AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology ImagesRuifen Cao0Qingbin Meng1Dayu Tan2Pijing Wei3Yun Ding4Chunhou Zheng5The Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, ChinaThe Information Materials and Intelligent Sensing Laboratory of Anhui Province, School of Computer Science and Technology, Anhui University, Hefei 230601, ChinaInstitutes of Physical Science and Information Technology, Anhui University, Hefei 230601, ChinaInstitutes of Physical Science and Information Technology, Anhui University, Hefei 230601, ChinaSchool of Artificial Intelligence, Anhui University, Hefei 230601, ChinaSchool of Artificial Intelligence, Anhui University, Hefei 230601, ChinaThe acurate segmentation and classification of nuclei in histological images are crucial for the diagnosis and treatment of colorectal cancer. However, the aggregation of nuclei and intra-class variability in histology images present significant challenges for nuclei segmentation and classification. In addition, the imbalance of various nuclei classes exacerbates the difficulty of nuclei classification and segmentation using deep learning models. To address these challenges, we present a novel attention-enhanced residual refinement network (AER-Net), which consists of one encoder and three decoder branches that have same network structure. In addition to the nuclei instance segmentation branch and nuclei classification branch, one branch is used to predict the vertical and horizontal distance from each pixel to its nuclear center, which is combined with output by the segmentation branch to improve the final segmentation results. The AER-Net utilizes an attention-enhanced encoder module to focus on more valuable features. To further refine predictions and achieve more accurate results, an attention-enhancing residual refinement module is employed at the end of each encoder branch. Moreover, the coarse predictions and refined predictions are combined by using a loss function that employs cross-entropy loss and generalized dice loss to efficiently tackle the challenge of class imbalance among nuclei in histology images. Compared with other state-of-the-art methods on two colorectal cancer datasets and a pan-cancer dataset, AER-Net demonstrates outstanding performance, validating its effectiveness in nuclear segmentation and classification.https://www.mdpi.com/1424-8220/24/22/7208histology imagesdeep learningnuclei segmentationnuclei classification |
| spellingShingle | Ruifen Cao Qingbin Meng Dayu Tan Pijing Wei Yun Ding Chunhou Zheng AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images Sensors histology images deep learning nuclei segmentation nuclei classification |
| title | AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images |
| title_full | AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images |
| title_fullStr | AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images |
| title_full_unstemmed | AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images |
| title_short | AER-Net: Attention-Enhanced Residual Refinement Network for Nuclei Segmentation and Classification in Histology Images |
| title_sort | aer net attention enhanced residual refinement network for nuclei segmentation and classification in histology images |
| topic | histology images deep learning nuclei segmentation nuclei classification |
| url | https://www.mdpi.com/1424-8220/24/22/7208 |
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