Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net
Computed tomography (CT) is widely used as the imaging modality for the treatment of tumors in Microwave Ablation (MWA) therapy. In order to accurately perform ablation of liver tumors and prevent tumor recurrence it is necessary to segment both the living tumor and the ablated tissue on the CT imag...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2022-09-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2022-1014 |
| _version_ | 1827998268794077184 |
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| author | Mahmoodian Naghmeh Thadesar Harshita Sadeghi Maryam Georgiades Marilena Pech Maciej Hoeschen Christoph |
| author_facet | Mahmoodian Naghmeh Thadesar Harshita Sadeghi Maryam Georgiades Marilena Pech Maciej Hoeschen Christoph |
| author_sort | Mahmoodian Naghmeh |
| collection | DOAJ |
| description | Computed tomography (CT) is widely used as the imaging modality for the treatment of tumors in Microwave Ablation (MWA) therapy. In order to accurately perform ablation of liver tumors and prevent tumor recurrence it is necessary to segment both the living tumor and the ablated tissue on the CT images. The U-Net model has outperformed other methods in biomedical image segmentation. However, because of the low contrast between tumor and liver tissue texture, the traditional U-net network cannot perform an accurate segmentation of the CT images of liver during MWA therapy. The aim of this study is to improve the U-net model network to achieve a higher segmentation performance on the CT images of liver tumor inMWA therapy. To achieve this, residual block is added in the first steps of up-sampling to deepen the network depth and enhance the segmentation result. We compare the proposed method named as ‘ResLU-Net’ with a conventional U-Net model. The results show that the ResLU-Net method has a good performance in tumor segmentation with a structure similarity index (SSIM) value of 0.97. This new method can help physicians in the MWA therapy process. |
| first_indexed | 2024-04-10T05:41:00Z |
| format | Article |
| id | doaj.art-5f33e7370b384ad0807116331f16fa65 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-04-10T05:41:00Z |
| publishDate | 2022-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-5f33e7370b384ad0807116331f16fa652023-03-06T10:24:51ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042022-09-0182495210.1515/cdbme-2022-1014Segmentation of Living and ablated Tumor parts in CT images Using ResLU-NetMahmoodian Naghmeh0Thadesar Harshita1Sadeghi Maryam2Georgiades Marilena3Pech Maciej4Hoeschen Christoph5Otto von Guericke University, Faculty of Electrical Engineering and Information Technology, Institute for Medical Technology, Otto-Hahn-Strasse 2,Magdeburg, GermanyOtto von Guericke University, Faculty of Electrical Engineering and Information Technology, Institute for Medical Technology,Magdeburg, GermanyMedical University of Innsbruck, Department of Medical Statistics, informatics and health economics,Innsbruck, AustriaOtto von Guericke University, Medical Faculty, University Clinic for Radiology and Nuclear Medicine,Magdeburg, GermanyOtto von Guericke University, Medical Faculty, University Clinic for Radiology and Nuclear Medicine,Magdeburg, GermanyOtto von Guericke University, Faculty of Electrical Engineering and Information Technology, Institute for Medical Technology,Magdeburg, GermanyComputed tomography (CT) is widely used as the imaging modality for the treatment of tumors in Microwave Ablation (MWA) therapy. In order to accurately perform ablation of liver tumors and prevent tumor recurrence it is necessary to segment both the living tumor and the ablated tissue on the CT images. The U-Net model has outperformed other methods in biomedical image segmentation. However, because of the low contrast between tumor and liver tissue texture, the traditional U-net network cannot perform an accurate segmentation of the CT images of liver during MWA therapy. The aim of this study is to improve the U-net model network to achieve a higher segmentation performance on the CT images of liver tumor inMWA therapy. To achieve this, residual block is added in the first steps of up-sampling to deepen the network depth and enhance the segmentation result. We compare the proposed method named as ‘ResLU-Net’ with a conventional U-Net model. The results show that the ResLU-Net method has a good performance in tumor segmentation with a structure similarity index (SSIM) value of 0.97. This new method can help physicians in the MWA therapy process.https://doi.org/10.1515/cdbme-2022-1014ct image of livercomputed tomographysegmentationu-netdeep learning |
| spellingShingle | Mahmoodian Naghmeh Thadesar Harshita Sadeghi Maryam Georgiades Marilena Pech Maciej Hoeschen Christoph Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net Current Directions in Biomedical Engineering ct image of liver computed tomography segmentation u-net deep learning |
| title | Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net |
| title_full | Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net |
| title_fullStr | Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net |
| title_full_unstemmed | Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net |
| title_short | Segmentation of Living and ablated Tumor parts in CT images Using ResLU-Net |
| title_sort | segmentation of living and ablated tumor parts in ct images using reslu net |
| topic | ct image of liver computed tomography segmentation u-net deep learning |
| url | https://doi.org/10.1515/cdbme-2022-1014 |
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