Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet
<p dir="ltr"><u>Digital pathology</u> relies on the morphological architecture of prostate glands to recognize cancerous tissue. <u>Prostate cancer</u> (PCa) originates in walnut shaped prostate gland in the <u>male reproductive system</u>. <u&g...
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2025
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| _version_ | 1864513532044771328 |
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| author | Md. Shaheenur Islam Sumon (22045907) |
| author2 | Muhammad E.H. Chowdhury (17151154) Enamul Hoque Bhuiyan (22803500) Md. Sohanur Rahman (8430495) Muntakim Mahmud Khan (22803503) Israa Al-Hashimi (18131374) Adam Mushtak (22048013) Sohaib Bassam Zoghoul (22803506) |
| author2_role | author author author author author author author |
| author_facet | Md. Shaheenur Islam Sumon (22045907) Muhammad E.H. Chowdhury (17151154) Enamul Hoque Bhuiyan (22803500) Md. Sohanur Rahman (8430495) Muntakim Mahmud Khan (22803503) Israa Al-Hashimi (18131374) Adam Mushtak (22048013) Sohaib Bassam Zoghoul (22803506) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Shaheenur Islam Sumon (22045907) Muhammad E.H. Chowdhury (17151154) Enamul Hoque Bhuiyan (22803500) Md. Sohanur Rahman (8430495) Muntakim Mahmud Khan (22803503) Israa Al-Hashimi (18131374) Adam Mushtak (22048013) Sohaib Bassam Zoghoul (22803506) |
| dc.date.none.fl_str_mv | 2025-07-30T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.compbiomed.2025.110459 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Multiclass_ensemble_framework_for_enhanced_prostate_gland_Segmentation_Integrating_Self-ONN_decoders_with_EfficientNet/30819533 |
| 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 Oncology and carcinogenesis Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Peripheral zone Transitional zone Self-ONN MRI Semantic segmentation Deep learning |
| dc.title.none.fl_str_mv | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr"><u>Digital pathology</u> relies on the morphological architecture of prostate glands to recognize cancerous tissue. <u>Prostate cancer</u> (PCa) originates in walnut shaped prostate gland in the <u>male reproductive system</u>. <u>Deep learning</u> (DL) pipelines can assist in identifying these regions with advanced <u>segmentation techniques</u> which are effective in diagnosing and treating <u>prostate diseases</u>. This facilitates early detection, targeted biopsy, and accurate treatment planning, ensuring consistent, reproducible results while minimizing human error. Automated segmentation techniques trained on MRI datasets can aid in monitoring disease progression which leads to clinical support by developing patient-specific models for personalized medicine. In this study, we present multiclass <u>segmentation models</u> designed to localize the prostate gland and its zonal regions—specifically the <u>peripheral zone</u> (PZ), transition zone (TZ), and the whole gland—by combining EfficientNetB4 encoders with Self-organized <u>Operational Neural Network</u> (Self-ONN)-based decoders. Traditional <u>convolutional neural networks</u> (CNNs) rely on linear neuron models, which limit their ability to capture the complex dynamics of biological neural systems. In contrast, Operational Neural Networks (ONNs), particularly Self-ONNs, address this limitation by incorporating nonlinear and adaptive operations at the neuron level. We evaluated various encoder-decoder configurations and identified that the combination of an EfficientNet-based encoder with a Self-ONN-based decoder yielded the best performance. To further enhance <u>segmentation accuracy</u>, we employed the STAPLE method to ensemble the top three performing models. Our approach was tested on the large-scale, recently updated PI-CAI Challenge dataset using 5-fold cross-validation, achieving Dice scores of 95.33 % for the whole gland and 92.32 % for the combined PZ and TZ regions. These advanced segmentation techniques significantly improve the quality of PCa diagnosis and treatment, contributing to better patient care and outcomes.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2025.110459" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2025.110459</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_26bd8d701fa26a1afb387b0abf76091f |
| identifier_str_mv | 10.1016/j.compbiomed.2025.110459 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30819533 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNetMd. Shaheenur Islam Sumon (22045907)Muhammad E.H. Chowdhury (17151154)Enamul Hoque Bhuiyan (22803500)Md. Sohanur Rahman (8430495)Muntakim Mahmud Khan (22803503)Israa Al-Hashimi (18131374)Adam Mushtak (22048013)Sohaib Bassam Zoghoul (22803506)Biomedical and clinical sciencesOncology and carcinogenesisEngineeringBiomedical engineeringHealth sciencesHealth services and systemsInformation and computing sciencesArtificial intelligenceMachine learningPeripheral zoneTransitional zoneSelf-ONNMRISemantic segmentationDeep learning<p dir="ltr"><u>Digital pathology</u> relies on the morphological architecture of prostate glands to recognize cancerous tissue. <u>Prostate cancer</u> (PCa) originates in walnut shaped prostate gland in the <u>male reproductive system</u>. <u>Deep learning</u> (DL) pipelines can assist in identifying these regions with advanced <u>segmentation techniques</u> which are effective in diagnosing and treating <u>prostate diseases</u>. This facilitates early detection, targeted biopsy, and accurate treatment planning, ensuring consistent, reproducible results while minimizing human error. Automated segmentation techniques trained on MRI datasets can aid in monitoring disease progression which leads to clinical support by developing patient-specific models for personalized medicine. In this study, we present multiclass <u>segmentation models</u> designed to localize the prostate gland and its zonal regions—specifically the <u>peripheral zone</u> (PZ), transition zone (TZ), and the whole gland—by combining EfficientNetB4 encoders with Self-organized <u>Operational Neural Network</u> (Self-ONN)-based decoders. Traditional <u>convolutional neural networks</u> (CNNs) rely on linear neuron models, which limit their ability to capture the complex dynamics of biological neural systems. In contrast, Operational Neural Networks (ONNs), particularly Self-ONNs, address this limitation by incorporating nonlinear and adaptive operations at the neuron level. We evaluated various encoder-decoder configurations and identified that the combination of an EfficientNet-based encoder with a Self-ONN-based decoder yielded the best performance. To further enhance <u>segmentation accuracy</u>, we employed the STAPLE method to ensemble the top three performing models. Our approach was tested on the large-scale, recently updated PI-CAI Challenge dataset using 5-fold cross-validation, achieving Dice scores of 95.33 % for the whole gland and 92.32 % for the combined PZ and TZ regions. These advanced segmentation techniques significantly improve the quality of PCa diagnosis and treatment, contributing to better patient care and outcomes.</p><h2>Other Information</h2><p dir="ltr">Published in: Computers in Biology and Medicine<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.compbiomed.2025.110459" target="_blank">https://dx.doi.org/10.1016/j.compbiomed.2025.110459</a></p>2025-07-30T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.compbiomed.2025.110459https://figshare.com/articles/journal_contribution/Multiclass_ensemble_framework_for_enhanced_prostate_gland_Segmentation_Integrating_Self-ONN_decoders_with_EfficientNet/30819533CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/308195332025-07-30T09:00:00Z |
| spellingShingle | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet Md. Shaheenur Islam Sumon (22045907) Biomedical and clinical sciences Oncology and carcinogenesis Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Peripheral zone Transitional zone Self-ONN MRI Semantic segmentation Deep learning |
| status_str | publishedVersion |
| title | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet |
| title_full | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet |
| title_fullStr | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet |
| title_full_unstemmed | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet |
| title_short | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet |
| title_sort | Multiclass ensemble framework for enhanced prostate gland Segmentation: Integrating Self-ONN decoders with EfficientNet |
| topic | Biomedical and clinical sciences Oncology and carcinogenesis Engineering Biomedical engineering Health sciences Health services and systems Information and computing sciences Artificial intelligence Machine learning Peripheral zone Transitional zone Self-ONN MRI Semantic segmentation Deep learning |