A lightweight neural network with multiscale feature enhancement for liver CT segmentation
<p dir="ltr">Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backb...
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| مؤلفون آخرون: | , , , , , , , , , , , , , , , |
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2022
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| _version_ | 1864513517987561472 |
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| author | Mohammed Yusuf Ansari (16904523) |
| author2 | Yin Yang (35103) Shidin Balakrishnan (14150580) Julien Abinahed (14151792) Abdulla Al-Ansari (14150583) Mohamed Warfa (18282250) Omran Almokdad (18282253) Ali Barah (14777533) Ahmed Omer (18282256) Ajay Vikram Singh (204056) Pramod Kumar Meher (17316988) Jolly Bhadra (14147823) Osama Halabi (14158905) Mohammad Farid Azampour (18282259) Nassir Navab (6254012) Thomas Wendler (13175656) Sarada Prasad Dakua (14151789) |
| author2_role | author author author author author author author author author author author author author author author author |
| author_facet | Mohammed Yusuf Ansari (16904523) Yin Yang (35103) Shidin Balakrishnan (14150580) Julien Abinahed (14151792) Abdulla Al-Ansari (14150583) Mohamed Warfa (18282250) Omran Almokdad (18282253) Ali Barah (14777533) Ahmed Omer (18282256) Ajay Vikram Singh (204056) Pramod Kumar Meher (17316988) Jolly Bhadra (14147823) Osama Halabi (14158905) Mohammad Farid Azampour (18282259) Nassir Navab (6254012) Thomas Wendler (13175656) Sarada Prasad Dakua (14151789) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammed Yusuf Ansari (16904523) Yin Yang (35103) Shidin Balakrishnan (14150580) Julien Abinahed (14151792) Abdulla Al-Ansari (14150583) Mohamed Warfa (18282250) Omran Almokdad (18282253) Ali Barah (14777533) Ahmed Omer (18282256) Ajay Vikram Singh (204056) Pramod Kumar Meher (17316988) Jolly Bhadra (14147823) Osama Halabi (14158905) Mohammad Farid Azampour (18282259) Nassir Navab (6254012) Thomas Wendler (13175656) Sarada Prasad Dakua (14151789) |
| dc.date.none.fl_str_mv | 2022-08-19T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-022-16828-6 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_lightweight_neural_network_with_multiscale_feature_enhancement_for_liver_CT_segmentation/25679862 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Machine learning Abdominal Computed Tomography (CT) Segmentation Visceral organ diseases Hepatocellular carcinoma Neural network Res-PAC-UNet |
| dc.title.none.fl_str_mv | A lightweight neural network with multiscale feature enhancement for liver CT segmentation |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.</p><p dir="ltr">Publisher Correction: A lightweight neural network with multiscale feature enhancement for liver CT segmentation: <a href="https://dx.doi.org/10.1038/s41598-022-20472-5" target="_blank">https://dx.doi.org/10.1038/s41598-022-20472-5</a>, published online 21 September 2022.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-16828-6" target="_blank">https://dx.doi.org/10.1038/s41598-022-16828-6</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_5c2345141344c484de9bbbb5bd1e7915 |
| identifier_str_mv | 10.1038/s41598-022-16828-6 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25679862 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A lightweight neural network with multiscale feature enhancement for liver CT segmentationMohammed Yusuf Ansari (16904523)Yin Yang (35103)Shidin Balakrishnan (14150580)Julien Abinahed (14151792)Abdulla Al-Ansari (14150583)Mohamed Warfa (18282250)Omran Almokdad (18282253)Ali Barah (14777533)Ahmed Omer (18282256)Ajay Vikram Singh (204056)Pramod Kumar Meher (17316988)Jolly Bhadra (14147823)Osama Halabi (14158905)Mohammad Farid Azampour (18282259)Nassir Navab (6254012)Thomas Wendler (13175656)Sarada Prasad Dakua (14151789)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringInformation and computing sciencesArtificial intelligenceMachine learningAbdominal Computed Tomography (CT)SegmentationVisceral organ diseasesHepatocellular carcinomaNeural networkRes-PAC-UNet<p dir="ltr">Segmentation of abdominal Computed Tomography (CT) scan is essential for analyzing, diagnosing, and treating visceral organ diseases (e.g., hepatocellular carcinoma). This paper proposes a novel neural network (Res-PAC-UNet) that employs a fixed-width residual UNet backbone and Pyramid Atrous Convolutions, providing a low disk utilization method for precise liver CT segmentation. The proposed network is trained on medical segmentation decathlon dataset using a modified surface loss function. Additionally, we evaluate its quantitative and qualitative performance; the Res16-PAC-UNet achieves a Dice coefficient of 0.950 ± 0.019 with less than half a million parameters. Alternatively, the Res32-PAC-UNet obtains a Dice coefficient of 0.958 ± 0.015 with an acceptable parameter count of approximately 1.2 million.</p><p dir="ltr">Publisher Correction: A lightweight neural network with multiscale feature enhancement for liver CT segmentation: <a href="https://dx.doi.org/10.1038/s41598-022-20472-5" target="_blank">https://dx.doi.org/10.1038/s41598-022-20472-5</a>, published online 21 September 2022.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-022-16828-6" target="_blank">https://dx.doi.org/10.1038/s41598-022-16828-6</a></p>2022-08-19T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-022-16828-6https://figshare.com/articles/journal_contribution/A_lightweight_neural_network_with_multiscale_feature_enhancement_for_liver_CT_segmentation/25679862CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256798622022-08-19T03:00:00Z |
| spellingShingle | A lightweight neural network with multiscale feature enhancement for liver CT segmentation Mohammed Yusuf Ansari (16904523) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Machine learning Abdominal Computed Tomography (CT) Segmentation Visceral organ diseases Hepatocellular carcinoma Neural network Res-PAC-UNet |
| status_str | publishedVersion |
| title | A lightweight neural network with multiscale feature enhancement for liver CT segmentation |
| title_full | A lightweight neural network with multiscale feature enhancement for liver CT segmentation |
| title_fullStr | A lightweight neural network with multiscale feature enhancement for liver CT segmentation |
| title_full_unstemmed | A lightweight neural network with multiscale feature enhancement for liver CT segmentation |
| title_short | A lightweight neural network with multiscale feature enhancement for liver CT segmentation |
| title_sort | A lightweight neural network with multiscale feature enhancement for liver CT segmentation |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Artificial intelligence Machine learning Abdominal Computed Tomography (CT) Segmentation Visceral organ diseases Hepatocellular carcinoma Neural network Res-PAC-UNet |