DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI
This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution (1 mm3) or using mono-modal data, the proposed method improves cerebellum lobule segmentation through the use of a multimodal and u...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-03-01
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| Series: | NeuroImage |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925000655 |
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| _version_ | 1823861213181771776 |
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| author | Sergio Morell-Ortega Marina Ruiz-Perez Marien Gadea Roberto Vivo-Hernando Gregorio Rubio Fernando Aparici Maria de la Iglesia-Vaya Gwenaelle Catheline Boris Mansencal Pierrick Coupé José V. Manjón |
| author_facet | Sergio Morell-Ortega Marina Ruiz-Perez Marien Gadea Roberto Vivo-Hernando Gregorio Rubio Fernando Aparici Maria de la Iglesia-Vaya Gwenaelle Catheline Boris Mansencal Pierrick Coupé José V. Manjón |
| author_sort | Sergio Morell-Ortega |
| collection | DOAJ |
| description | This paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution (1 mm3) or using mono-modal data, the proposed method improves cerebellum lobule segmentation through the use of a multimodal and ultra-high resolution (0.125 mm3) training dataset. To develop the method, first, a database of semi-automatically labelled cerebellum lobules was created to train the proposed method with ultra-high resolution T1 and T2 MR images. Then, an ensemble of deep networks has been designed and developed, allowing the proposed method to excel in the complex cerebellum lobule segmentation task, improving precision while being memory efficient. Notably, our approach deviates from the traditional U-Net model by exploring alternative architectures. We have also integrated deep learning with classical machine learning methods incorporating a priori knowledge from multi-atlas segmentation which improved precision and robustness. Finally, a new online pipeline, named DeepCERES, has been developed to make available the proposed method to the scientific community requiring as input only a single T1 MR image at standard resolution. |
| format | Article |
| id | doaj-art-cfbb42878a864115b75d6966d560d9a6 |
| institution | Kabale University |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-cfbb42878a864115b75d6966d560d9a62025-02-10T04:34:13ZengElsevierNeuroImage1095-95722025-03-01308121063DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRISergio Morell-Ortega0Marina Ruiz-Perez1Marien Gadea2Roberto Vivo-Hernando3Gregorio Rubio4Fernando Aparici5Maria de la Iglesia-Vaya6Gwenaelle Catheline7Boris Mansencal8Pierrick Coupé9José V. Manjón10Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, Spain; Corresponding author.Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, SpainDepartment of Psychobiology, Faculty of Psychology, Universitat de Valencia, Valencia, SpainInstituto de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, SpainDepartamento de matemática aplicada, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, SpainÁrea de Imagen Médica. Hospital Universitario y Politécnico La Fe. Valencia, SpainUnidad Mixta de Imagen Biomédica FISABIO-CIPF. Fundación para el Fomento de la Investigación Sanitario y Biomédica de la Comunidad Valenciana - Valencia, SpainUniv. Bordeaux, CNRS, UMR 5287, Institut de Neurosciences Cognitives et Intégratives d’Aquitaine, Bordeaux, FranceCNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, in2brain, F-33400 Talence, FranceCNRS, Univ. Bordeaux, Bordeaux INP, LABRI, UMR5800, in2brain, F-33400 Talence, FranceInstituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022, Valencia, SpainThis paper introduces a novel multimodal and high-resolution human brain cerebellum lobule segmentation method. Unlike current tools that operate at standard resolution (1 mm3) or using mono-modal data, the proposed method improves cerebellum lobule segmentation through the use of a multimodal and ultra-high resolution (0.125 mm3) training dataset. To develop the method, first, a database of semi-automatically labelled cerebellum lobules was created to train the proposed method with ultra-high resolution T1 and T2 MR images. Then, an ensemble of deep networks has been designed and developed, allowing the proposed method to excel in the complex cerebellum lobule segmentation task, improving precision while being memory efficient. Notably, our approach deviates from the traditional U-Net model by exploring alternative architectures. We have also integrated deep learning with classical machine learning methods incorporating a priori knowledge from multi-atlas segmentation which improved precision and robustness. Finally, a new online pipeline, named DeepCERES, has been developed to make available the proposed method to the scientific community requiring as input only a single T1 MR image at standard resolution.http://www.sciencedirect.com/science/article/pii/S1053811925000655 |
| spellingShingle | Sergio Morell-Ortega Marina Ruiz-Perez Marien Gadea Roberto Vivo-Hernando Gregorio Rubio Fernando Aparici Maria de la Iglesia-Vaya Gwenaelle Catheline Boris Mansencal Pierrick Coupé José V. Manjón DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI NeuroImage |
| title | DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI |
| title_full | DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI |
| title_fullStr | DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI |
| title_full_unstemmed | DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI |
| title_short | DeepCERES: A deep learning method for cerebellar lobule segmentation using ultra-high resolution multimodal MRI |
| title_sort | deepceres a deep learning method for cerebellar lobule segmentation using ultra high resolution multimodal mri |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925000655 |
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