Learning to detect boundary information for brain image segmentation
Abstract MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix proper...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2022-08-01
|
| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04882-w |
| _version_ | 1818084348236136448 |
|---|---|
| author | Afifa Khaled Jian-Jun Han Taher A. Ghaleb |
| author_facet | Afifa Khaled Jian-Jun Han Taher A. Ghaleb |
| author_sort | Afifa Khaled |
| collection | DOAJ |
| description | Abstract MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to $$5.3\%$$ 5.3 % compared to the state-of-the-art models) in detecting and segmenting brain tissue images. |
| first_indexed | 2024-12-10T19:52:27Z |
| format | Article |
| id | doaj.art-03da7393c9074c5b9f2d5250ec2bc852 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T19:52:27Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-03da7393c9074c5b9f2d5250ec2bc8522022-12-22T01:35:44ZengBMCBMC Bioinformatics1471-21052022-08-0123111510.1186/s12859-022-04882-wLearning to detect boundary information for brain image segmentationAfifa Khaled0Jian-Jun Han1Taher A. Ghaleb2School of Computer Science and Technology, Huazhong University of Science and TechnologySchool of Computer Science and Technology, Huazhong University of Science and TechnologySchool of Electrical Engineering and Computer Science, University of OttawaAbstract MRI brain images are always of low contrast, which makes it difficult to identify to which area the information at the boundary of brain images belongs. This can make the extraction of features at the boundary more challenging, since those features can be misleading as they might mix properties of different brain regions. Hence, to alleviate such a problem, image boundary detection plays a vital role in medical image segmentation, and brain segmentation in particular, as unclear boundaries can worsen brain segmentation results. Yet, given the low quality of brain images, boundary detection in the context of brain image segmentation remains challenging. Despite the research invested to improve boundary detection and brain segmentation, these two problems were addressed independently, i.e., little attention was paid to applying boundary detection to brain segmentation tasks. Therefore, in this paper, we propose a boundary detection-based model for brain image segmentation. To this end, we first design a boundary segmentation network for detecting and segmenting images brain tissues. Then, we design a boundary information module (BIM) to distinguish boundaries from the three different brain tissues. After that, we add a boundary attention gate (BAG) to the encoder output layers of our transformer to capture more informative local details. We evaluate our proposed model on two datasets of brain tissue images, including infant and adult brains. The extensive evaluation experiments of our model show better performance (a Dice Coefficient (DC) accuracy of up to $$5.3\%$$ 5.3 % compared to the state-of-the-art models) in detecting and segmenting brain tissue images.https://doi.org/10.1186/s12859-022-04882-wMedical imagingBoundary detectionBrain segmentationMRI |
| spellingShingle | Afifa Khaled Jian-Jun Han Taher A. Ghaleb Learning to detect boundary information for brain image segmentation BMC Bioinformatics Medical imaging Boundary detection Brain segmentation MRI |
| title | Learning to detect boundary information for brain image segmentation |
| title_full | Learning to detect boundary information for brain image segmentation |
| title_fullStr | Learning to detect boundary information for brain image segmentation |
| title_full_unstemmed | Learning to detect boundary information for brain image segmentation |
| title_short | Learning to detect boundary information for brain image segmentation |
| title_sort | learning to detect boundary information for brain image segmentation |
| topic | Medical imaging Boundary detection Brain segmentation MRI |
| url | https://doi.org/10.1186/s12859-022-04882-w |
| work_keys_str_mv | AT afifakhaled learningtodetectboundaryinformationforbrainimagesegmentation AT jianjunhan learningtodetectboundaryinformationforbrainimagesegmentation AT taheraghaleb learningtodetectboundaryinformationforbrainimagesegmentation |