Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement
Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmenta...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/10/8/1468 |
| _version_ | 1797432165347098624 |
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| author | Luna Wang Liao Yu Jun Zhu Haoyu Tang Fangfang Gou Jia Wu |
| author_facet | Luna Wang Liao Yu Jun Zhu Haoyu Tang Fangfang Gou Jia Wu |
| author_sort | Luna Wang |
| collection | DOAJ |
| description | Osteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis. |
| first_indexed | 2024-03-09T09:56:21Z |
| format | Article |
| id | doaj.art-64e352e9632c4df8b0b1909bbcaf8e63 |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-09T09:56:21Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
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| series | Healthcare |
| spelling | doaj.art-64e352e9632c4df8b0b1909bbcaf8e632023-12-01T23:45:18ZengMDPI AGHealthcare2227-90322022-08-01108146810.3390/healthcare10081468Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local EnhancementLuna Wang0Liao Yu1Jun Zhu2Haoyu Tang3Fangfang Gou4Jia Wu5School of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaThe First People’s Hospital of Huaihua, Huaihua 418099, ChinaThe First People’s Hospital of Huaihua, Huaihua 418099, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaSchool of Computer Science and Engineering, Central South University, Changsha 410083, ChinaOsteosarcoma is a bone tumor which is malignant. There are many difficulties when doctors manually identify patients’ MRI images to complete the diagnosis. The osteosarcoma in MRI images is very complex, making its recognition and segmentation resource-consuming. Automatic osteosarcoma area segmentation can solve these problems to a certain extent. However, existing studies usually fail to balance segmentation accuracy and efficiency. They are either sensitive to noise with low accuracy or time-consuming. So we propose an auxiliary segmentation method based on denoising and local enhancement. The method first optimizes the osteosarcoma images, including removing noise using the Edge Enhancement based Transformer for Medical Image Denoising (Eformer) and using a non-parameter method to localize and enhance the tumor region in MRI images. Osteosarcoma was then segmented by Deep Feature Aggregation for Real-Time Semantic Segmentation (DFANet). Our method achieves impressive segmentation accuracy. Moreover, it is efficient in both time and space. It can provide information about the location and extent of the osteosarcoma as a basis for further diagnosis.https://www.mdpi.com/2227-9032/10/8/1468image segmentationmachine learningnon-parameterlocalizationenhancementdenoising |
| spellingShingle | Luna Wang Liao Yu Jun Zhu Haoyu Tang Fangfang Gou Jia Wu Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement Healthcare image segmentation machine learning non-parameter localization enhancement denoising |
| title | Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement |
| title_full | Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement |
| title_fullStr | Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement |
| title_full_unstemmed | Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement |
| title_short | Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement |
| title_sort | auxiliary segmentation method of osteosarcoma in mri images based on denoising and local enhancement |
| topic | image segmentation machine learning non-parameter localization enhancement denoising |
| url | https://www.mdpi.com/2227-9032/10/8/1468 |
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