MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data
The existing methods for accurate brain tumor (BT) segmentation based on homogeneous datasets show significant performance degradation in actual clinical applications and lacked heterogeneous data analysis. To address these issues, we designed a deep learning-based multiscale dilated features up-sam...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-05-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823001064 |
| _version_ | 1827939504897392640 |
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| author | Haseeb Sultan Muhammad Owais Se Hyun Nam Adnan Haider Rehan Akram Muhammad Usman Kang Ryoung Park |
| author_facet | Haseeb Sultan Muhammad Owais Se Hyun Nam Adnan Haider Rehan Akram Muhammad Usman Kang Ryoung Park |
| author_sort | Haseeb Sultan |
| collection | DOAJ |
| description | The existing methods for accurate brain tumor (BT) segmentation based on homogeneous datasets show significant performance degradation in actual clinical applications and lacked heterogeneous data analysis. To address these issues, we designed a deep learning-based multiscale dilated features up-sampling network (MDFU-Net) for accurate BT segmentation from heterogeneous brain data. Our method primarily uses the strength of multiscale dilated features (MDF) inside the encoder module to improve the segmentation performance. For the final segmentation, a simple yet effective decoder module is designed to process the dense spatial MDF. For experiments, our MDFU-Net is trained on one dataset and tested with another dataset in a heterogeneous environment, showing quantitative results of the Dice similarity coefficient (DC) of 62.66%, intersection over union (IoU) of 56.96%, specificity (Spe) of 99.29%, and sensitivity (Sen) of 51.98%, which were higher than those of the state-of-the-art methods. There are several reasons for the lower values of the evaluation metrics of the heterogeneous dataset, including the change in characteristics of different MRI modalities, the presence of minor lesions, and a highly imbalanced dataset. Moreover, the experimental results for a homogeneous dataset showed that our MDFU-Net achieved a DC of 82.96%, IoU of 74.94%, Spe of 99.89%, and Sen of 68.05%, which were also higher than those of the state-of-the-art methods. Our system, which is based on heterogeneous brain data as well as homogeneous brain data, can be advantageous to radiologists and medical experts. |
| first_indexed | 2024-03-13T08:54:32Z |
| format | Article |
| id | doaj.art-9e7e49f984d94324ae90cdbcf53dbf00 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-13T08:54:32Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-9e7e49f984d94324ae90cdbcf53dbf002023-05-29T04:03:48ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-05-01355101560MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain dataHaseeb Sultan0Muhammad Owais1Se Hyun Nam2Adnan Haider3Rehan Akram4Muhammad Usman5Kang Ryoung Park6Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South KoreaDivision of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South KoreaCorresponding author.; Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 04620, South KoreaThe existing methods for accurate brain tumor (BT) segmentation based on homogeneous datasets show significant performance degradation in actual clinical applications and lacked heterogeneous data analysis. To address these issues, we designed a deep learning-based multiscale dilated features up-sampling network (MDFU-Net) for accurate BT segmentation from heterogeneous brain data. Our method primarily uses the strength of multiscale dilated features (MDF) inside the encoder module to improve the segmentation performance. For the final segmentation, a simple yet effective decoder module is designed to process the dense spatial MDF. For experiments, our MDFU-Net is trained on one dataset and tested with another dataset in a heterogeneous environment, showing quantitative results of the Dice similarity coefficient (DC) of 62.66%, intersection over union (IoU) of 56.96%, specificity (Spe) of 99.29%, and sensitivity (Sen) of 51.98%, which were higher than those of the state-of-the-art methods. There are several reasons for the lower values of the evaluation metrics of the heterogeneous dataset, including the change in characteristics of different MRI modalities, the presence of minor lesions, and a highly imbalanced dataset. Moreover, the experimental results for a homogeneous dataset showed that our MDFU-Net achieved a DC of 82.96%, IoU of 74.94%, Spe of 99.89%, and Sen of 68.05%, which were also higher than those of the state-of-the-art methods. Our system, which is based on heterogeneous brain data as well as homogeneous brain data, can be advantageous to radiologists and medical experts.http://www.sciencedirect.com/science/article/pii/S1319157823001064Brain tumorComputer-aided diagnosisDeep learningHeterogeneous dataMDFU-Net |
| spellingShingle | Haseeb Sultan Muhammad Owais Se Hyun Nam Adnan Haider Rehan Akram Muhammad Usman Kang Ryoung Park MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data Journal of King Saud University: Computer and Information Sciences Brain tumor Computer-aided diagnosis Deep learning Heterogeneous data MDFU-Net |
| title | MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data |
| title_full | MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data |
| title_fullStr | MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data |
| title_full_unstemmed | MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data |
| title_short | MDFU-Net: Multiscale dilated features up-sampling network for accurate segmentation of tumor from heterogeneous brain data |
| title_sort | mdfu net multiscale dilated features up sampling network for accurate segmentation of tumor from heterogeneous brain data |
| topic | Brain tumor Computer-aided diagnosis Deep learning Heterogeneous data MDFU-Net |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823001064 |
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