RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm
Abstract Background Glaucoma can cause irreversible blindness to people’s eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In re...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-12-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-05058-2 |
| _version_ | 1828121704843444224 |
|---|---|
| author | Yun Jiang Zeqi Ma Chao Wu Zequn Zhang Wei Yan |
| author_facet | Yun Jiang Zeqi Ma Chao Wu Zequn Zhang Wei Yan |
| author_sort | Yun Jiang |
| collection | DOAJ |
| description | Abstract Background Glaucoma can cause irreversible blindness to people’s eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough. Methods This paper proposes a new encoder–decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder–decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup. Results The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively. Conclusions This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective. |
| first_indexed | 2024-04-11T14:24:23Z |
| format | Article |
| id | doaj.art-5f0217093582497294802a90e1aec2e8 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-11T14:24:23Z |
| publishDate | 2022-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-5f0217093582497294802a90e1aec2e82022-12-22T04:18:56ZengBMCBMC Bioinformatics1471-21052022-12-0123112110.1186/s12859-022-05058-2RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithmYun Jiang0Zeqi Ma1Chao Wu2Zequn Zhang3Wei Yan4Department of Computer Science and Engineering, Northwest Normal UniversityDepartment of Computer Science and Engineering, Northwest Normal UniversityDepartment of Computer Science and Engineering, Northwest Normal UniversityDepartment of Computer Science and Engineering, Northwest Normal UniversityDepartment of Computer Science and Engineering, Northwest Normal UniversityAbstract Background Glaucoma can cause irreversible blindness to people’s eyesight. Since there are no symptoms in its early stage, it is particularly important to accurately segment the optic disc (OD) and optic cup (OC) from fundus medical images for the screening and prevention of glaucoma. In recent years, the mainstream method of OD and OC segmentation is convolution neural network (CNN). However, most existing CNN methods segment OD and OC separately and ignore the a priori information that OC is always contained inside the OD region, which makes the segmentation accuracy of most methods not high enough. Methods This paper proposes a new encoder–decoder segmentation structure, called RSAP-Net, for joint segmentation of OD and OC. We first designed an efficient U-shaped segmentation network as the backbone. Considering the spatial overlap relationship between OD and OC, a new Residual spatial attention path is proposed to connect the encoder–decoder to retain more characteristic information. In order to further improve the segmentation performance, a pre-processing method called MSRCR-PT (Multi-Scale Retinex Colour Recovery and Polar Transformation) has been devised. It incorporates a multi-scale Retinex colour recovery algorithm and a polar coordinate transformation, which can help RSAP-Net to produce more refined boundaries of the optic disc and the optic cup. Results The experimental results show that our method achieves excellent segmentation performance on the Drishti-GS1 standard dataset. In the OD and OC segmentation effects, the F1 scores are 0.9752 and 0.9012, respectively. The BLE are 6.33 pixels and 11.97 pixels, respectively. Conclusions This paper presents a new framework for the joint segmentation of optic discs and optic cups, called RSAP-Net. The framework mainly consists of a U-shaped segmentation skeleton and a residual space attention path module. The design of a pre-processing method called MSRCR-PT for the OD/OC segmentation task can improve segmentation performance. The method was evaluated on the publicly available Drishti-GS1 standard dataset and proved to be effective.https://doi.org/10.1186/s12859-022-05058-2Joint optic disc and cup segmentationGlaucoma screeningConvolutional neural workAttention mechanismPre-processingRetinex theory |
| spellingShingle | Yun Jiang Zeqi Ma Chao Wu Zequn Zhang Wei Yan RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm BMC Bioinformatics Joint optic disc and cup segmentation Glaucoma screening Convolutional neural work Attention mechanism Pre-processing Retinex theory |
| title | RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm |
| title_full | RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm |
| title_fullStr | RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm |
| title_full_unstemmed | RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm |
| title_short | RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT pre-processing algorithm |
| title_sort | rsap net joint optic disc and cup segmentation with a residual spatial attention path module and msrcr pt pre processing algorithm |
| topic | Joint optic disc and cup segmentation Glaucoma screening Convolutional neural work Attention mechanism Pre-processing Retinex theory |
| url | https://doi.org/10.1186/s12859-022-05058-2 |
| work_keys_str_mv | AT yunjiang rsapnetjointopticdiscandcupsegmentationwitharesidualspatialattentionpathmoduleandmsrcrptpreprocessingalgorithm AT zeqima rsapnetjointopticdiscandcupsegmentationwitharesidualspatialattentionpathmoduleandmsrcrptpreprocessingalgorithm AT chaowu rsapnetjointopticdiscandcupsegmentationwitharesidualspatialattentionpathmoduleandmsrcrptpreprocessingalgorithm AT zequnzhang rsapnetjointopticdiscandcupsegmentationwitharesidualspatialattentionpathmoduleandmsrcrptpreprocessingalgorithm AT weiyan rsapnetjointopticdiscandcupsegmentationwitharesidualspatialattentionpathmoduleandmsrcrptpreprocessingalgorithm |