Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models
Adrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. We imp...
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2025-05-01
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| author | Vlad-Octavian Bolocan Oana Nicu-Canareica Alexandru Mitoi Maria Glencora Costache Loredana Sabina Cornelia Manolescu Cosmin Medar Viorel Jinga |
| author_facet | Vlad-Octavian Bolocan Oana Nicu-Canareica Alexandru Mitoi Maria Glencora Costache Loredana Sabina Cornelia Manolescu Cosmin Medar Viorel Jinga |
| author_sort | Vlad-Octavian Bolocan |
| collection | DOAJ |
| description | Adrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. We implemented three convolutional neural network architectures (U-Net, SegNet, and NablaNet) and assessed their performance on a dataset comprising 868 adrenal glands from contrast-enhanced abdominal CT scans. Performance was evaluated using the Dice similarity coefficient (DSC), alongside practical implementation metrics including training and deployment time. U-Net demonstrated superior segmentation performance (DSC: 0.630 ± 0.05 for right, 0.660 ± 0.06 for left adrenal glands) compared to NablaNet (DSC: 0.552 ± 0.08 for right, 0.550 ± 0.07 for left) and SegNet (DSC: 0.320 ± 0.10 for right, 0.335 ± 0.09 for left). While all models achieved high specificity, boundary delineation accuracy remained challenging. Our findings demonstrate the feasibility of deep learning-based adrenal gland segmentation while highlighting the persistent challenges in achieving the segmentation quality observed with larger abdominal organs. U-Net provides the optimal balance between accuracy and computational requirements, establishing a foundation for further refinement of AI-assisted adrenal imaging tools. |
| format | Article |
| id | doaj-art-8b920da39e784911a7af67d1f2b0a729 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-8b920da39e784911a7af67d1f2b0a7292025-08-20T03:14:35ZengMDPI AGApplied Sciences2076-34172025-05-011510538810.3390/app15105388Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network ModelsVlad-Octavian Bolocan0Oana Nicu-Canareica1Alexandru Mitoi2Maria Glencora Costache3Loredana Sabina Cornelia Manolescu4Cosmin Medar5Viorel Jinga6Doctoral Program Studies, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, RomaniaDepartment of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, RomaniaDoctoral Program Studies, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, RomaniaDepartment of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, RomaniaDepartment of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, RomaniaDepartment of Fundamental Sciences, Faculty of Midwifery and Nursing, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, RomaniaDepartment of Urology, Clinical Hospital “Prof. Dr. Theodor Burghele”, Faculty of Medicine, University of Medicine and Pharmacy “Carol Davila”, 050474 Bucharest, RomaniaAdrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. We implemented three convolutional neural network architectures (U-Net, SegNet, and NablaNet) and assessed their performance on a dataset comprising 868 adrenal glands from contrast-enhanced abdominal CT scans. Performance was evaluated using the Dice similarity coefficient (DSC), alongside practical implementation metrics including training and deployment time. U-Net demonstrated superior segmentation performance (DSC: 0.630 ± 0.05 for right, 0.660 ± 0.06 for left adrenal glands) compared to NablaNet (DSC: 0.552 ± 0.08 for right, 0.550 ± 0.07 for left) and SegNet (DSC: 0.320 ± 0.10 for right, 0.335 ± 0.09 for left). While all models achieved high specificity, boundary delineation accuracy remained challenging. Our findings demonstrate the feasibility of deep learning-based adrenal gland segmentation while highlighting the persistent challenges in achieving the segmentation quality observed with larger abdominal organs. U-Net provides the optimal balance between accuracy and computational requirements, establishing a foundation for further refinement of AI-assisted adrenal imaging tools.https://www.mdpi.com/2076-3417/15/10/5388deep learningadrenal glandCNN models |
| spellingShingle | Vlad-Octavian Bolocan Oana Nicu-Canareica Alexandru Mitoi Maria Glencora Costache Loredana Sabina Cornelia Manolescu Cosmin Medar Viorel Jinga Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models Applied Sciences deep learning adrenal gland CNN models |
| title | Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models |
| title_full | Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models |
| title_fullStr | Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models |
| title_full_unstemmed | Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models |
| title_short | Deep Learning for Adrenal Gland Segmentation: Comparing Accuracy and Efficiency Across Three Convolutional Neural Network Models |
| title_sort | deep learning for adrenal gland segmentation comparing accuracy and efficiency across three convolutional neural network models |
| topic | deep learning adrenal gland CNN models |
| url | https://www.mdpi.com/2076-3417/15/10/5388 |
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