Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation
Abstract There is growing interest in research on segmentation for the vestibular schwannoma (VS) and cochlea using high-resolution T2 (hrT2) imaging over contrast-enhanced T1 (ceT1) imaging due to the contrast agent side effects. However, the hrT2 imaging remains a problem of insufficient annotated...
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-77633-x |
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| author | Bogyeong Kang Hyeonyeong Nam Myeongkyun Kang Keun-Soo Heo Minjoo Lim Ji-Hye Oh Tae-Eui Kam |
| author_facet | Bogyeong Kang Hyeonyeong Nam Myeongkyun Kang Keun-Soo Heo Minjoo Lim Ji-Hye Oh Tae-Eui Kam |
| author_sort | Bogyeong Kang |
| collection | DOAJ |
| description | Abstract There is growing interest in research on segmentation for the vestibular schwannoma (VS) and cochlea using high-resolution T2 (hrT2) imaging over contrast-enhanced T1 (ceT1) imaging due to the contrast agent side effects. However, the hrT2 imaging remains a problem of insufficient annotated data, which is fatal for building more robust segmentation models. To address the issue, recent studies have adopted unsupervised domain adaptation approaches that translate ceT1 images to hrT2 images. However, previous studies did not consider the size and visual characteristics of the target objects, such as VS and cochlea, during image translation. Specifically, those works simply performed normalization on the entire image without considering its significant impact on the quality of the translated images. These approaches tend to erase the small target objects, making it difficult to preserve the structure of these objects when generating pseudo-target images. Furthermore, they may also struggle to accurately reflect the unique style of the target objects within the images. Therefore, we propose a target-aware unsupervised domain adaptation framework, designed for translating target objects, each tailored to their unique visual characteristics and size using target-aware normalization. We demonstrate the superiority of the proposed framework on a publicly available challenge dataset. Codes are available at https://github.com/Bokyeong-Kang/TANQ . |
| format | Article |
| id | doaj-art-37106a6db6f84a5eb0fea6db9ceef1e6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-37106a6db6f84a5eb0fea6db9ceef1e62024-11-17T12:21:15ZengNature PortfolioScientific Reports2045-23222024-11-0114111210.1038/s41598-024-77633-xTarget-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentationBogyeong Kang0Hyeonyeong Nam1Myeongkyun Kang2Keun-Soo Heo3Minjoo Lim4Ji-Hye Oh5Tae-Eui Kam6Department of Artificial Intelligence, Korea UniversityDepartment of Artificial Intelligence, Korea UniversityDepartment of Robotics and Mechatronics Engineering, Daegu Gyeongbuk Institute of Science and Technology (DGIST)Department of Artificial Intelligence, Korea UniversityDepartment of Artificial Intelligence, Korea UniversityDepartment of Artificial Intelligence, Korea UniversityDepartment of Artificial Intelligence, Korea UniversityAbstract There is growing interest in research on segmentation for the vestibular schwannoma (VS) and cochlea using high-resolution T2 (hrT2) imaging over contrast-enhanced T1 (ceT1) imaging due to the contrast agent side effects. However, the hrT2 imaging remains a problem of insufficient annotated data, which is fatal for building more robust segmentation models. To address the issue, recent studies have adopted unsupervised domain adaptation approaches that translate ceT1 images to hrT2 images. However, previous studies did not consider the size and visual characteristics of the target objects, such as VS and cochlea, during image translation. Specifically, those works simply performed normalization on the entire image without considering its significant impact on the quality of the translated images. These approaches tend to erase the small target objects, making it difficult to preserve the structure of these objects when generating pseudo-target images. Furthermore, they may also struggle to accurately reflect the unique style of the target objects within the images. Therefore, we propose a target-aware unsupervised domain adaptation framework, designed for translating target objects, each tailored to their unique visual characteristics and size using target-aware normalization. We demonstrate the superiority of the proposed framework on a publicly available challenge dataset. Codes are available at https://github.com/Bokyeong-Kang/TANQ .https://doi.org/10.1038/s41598-024-77633-x |
| spellingShingle | Bogyeong Kang Hyeonyeong Nam Myeongkyun Kang Keun-Soo Heo Minjoo Lim Ji-Hye Oh Tae-Eui Kam Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation Scientific Reports |
| title | Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation |
| title_full | Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation |
| title_fullStr | Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation |
| title_full_unstemmed | Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation |
| title_short | Target-aware cross-modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation |
| title_sort | target aware cross modality unsupervised domain adaptation for vestibular schwannoma and cochlea segmentation |
| url | https://doi.org/10.1038/s41598-024-77633-x |
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