- -

A novel deep learning based hippocampus subfield segmentation method

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

A novel deep learning based hippocampus subfield segmentation method

Mostrar el registro completo del ítem

Manjón Herrera, JV.; Romero, JE.; Coupe, P. (2022). A novel deep learning based hippocampus subfield segmentation method. Scientific Reports. 12(1):1-9. https://doi.org/10.1038/s41598-022-05287-8

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/194517

Ficheros en el ítem

Metadatos del ítem

Título: A novel deep learning based hippocampus subfield segmentation method
Autor: Manjón Herrera, José Vicente Romero, José E. Coupe, Pierrick
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[EN] The automatic assessment of hippocampus volume is an important tool in the study of several neurodegenerative diseases such as Alzheimer's disease. Specifically, the measurement of hippocampus subfields properties is ...[+]
Palabras clave: MRI , Hipocampus , Segmentation
Derechos de uso: Reconocimiento (by)
Fuente:
Scientific Reports. (issn: 2045-2322 )
DOI: 10.1038/s41598-022-05287-8
Editorial:
Nature Publishing Group
Versión del editor: https://doi.org/10.1038/s41598-022-05287-8
Código del Proyecto:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-87743-R/ES/DESARROLLO DE UNA PLATAFORMA ONLINE PARA EL ANALISIS ANATOMICO DEL CEREBRO TOLERANTE A LA PRESENCIA DE ALTERACIONES PATOLOGICAS/
info:eu-repo/grantAgreement/ANR//ANR-10-IDEX-03-02/
info:eu-repo/grantAgreement/ANR//ANR-10-LABX-57/
Agradecimientos:
This research was supported by the Spanish DPI2017-87743-R grant from the Ministerio de Economia, Industria y Competitividad of Spain. This study has been also carried out with financial support from the French State, ...[+]
Tipo: Artículo

References

Milner, B. Psychological defects produced by temporal lobe excision. Res. Publ. Assoc. Res. Nerv. Ment. Dis. 36, 244–257 (1958).

Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82(4), 239–259 (1991).

Jack, C. R. et al. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 55, 484–489 (2000). [+]
Milner, B. Psychological defects produced by temporal lobe excision. Res. Publ. Assoc. Res. Nerv. Ment. Dis. 36, 244–257 (1958).

Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82(4), 239–259 (1991).

Jack, C. R. et al. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology 55, 484–489 (2000).

Jack, C. R. et al. Brain atrophy rates predict subsequent clinical conversion in normal elderly and amnestic MCI. Neurology 65, 1227–1231 (2005).

Dickerson, B. C. & Sperling, R. A. Neuroimaging biomarkers for clinical trials of disease-modifying therapies in Alzheimer’s disease. NeuroRx 2, 348–360 (2005).

Barnes, J. et al. A comparison of methods for the automated calculation of volumes and atrophy rates in the hippocampus. Neuroimage 40, 1655–1671 (2008).

Collins, D. L. & Pruessner, J. C. Towards accurate, automatic segmentation of the hippocampus and amygdala from MRI by augmenting ANIMAL with a template library and label fusion. Neuroimage 52(4), 1355–1366 (2010).

Coupé, P. et al. Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. Neuroimage 54(2), 940–954 (2011).

Chupin, M. et al. Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus 19(6), 579–587 (2009).

Hett, K., Ta, V., Catheline, G., Tourdias, T., Manjón, J. V., Coupe, P. Multimodal Hippocampal Subfield Grading For Alzheimer’s Disease Classification. (Scientific Reports, 2019).

Yushkevich, P. A. et al. Quantitative comparison of 21 protocols for labeling hippocampal subfields and parahippocampal subregions in in vivo MRI: Towards a harmonized segmentation protocol. Neuroimage 111, 526–541 (2015).

Winterburn, J. L. et al. A novel in vivo atlas of human hippocampal subfields using high-resolution 3 T magnetic resonance imaging. Neuroimage 74, 254–265 (2013).

Kulaga-Yoskovitz, J. et al. Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset. Sci. Data. 2, 150059 (2015).

Van Leemput, K. et al. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus 19(6), 549–557 (2009).

Pipitone, J. et al. Multi-atlas segmentation of the whole hippocampus and subfields using multiple automatically generated templates. Neuroimage 101, 494–512 (2014).

Iglesias, J. E. et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: Application to adaptive segmentation of in vivo MRI. Neuroimage 115(15), 117–137 (2015).

Yushkevich, P. A. et al. Automated volumetry and regional thickness analysis of hippocampal subfields and medial temporal cortical structures in mild cognitive impairment. Hum. Brain Mapp. 36(1), 258–287 (2015).

Romero, J. E., Coupé, P. & Manjón, J. V. HIPS: A new hippocampus subfield segmentation method. Neuroimage 163, 286–295 (2017).

Giraud, R. et al. An optimized PatchMatch for multi-scale and multi-feature label fusion. Neuroimage 124, 770–782 (2016).

Peixoto-Santos, J. E. et al. Manual hippocampal subfield segmentation using high-field MRI: Impact of different subfields in hippocampal volume loss of temporal lobe epilepsy patients. Front. Neurol. 9, 927 (2018).

Chen, Y., Shi, B., Wang, Z., Zhang, P., Smith, C. D., & Liu, J. Hippocampus segmentation through multi-view ensemble ConvNets. In 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 192–196 (Melbourne, VIC, 2017).

Cao, L. et al. Multi-task neural networks for joint hippocampus segmentation and clinical score regression. Multimed. Tools Appl. 77(22), 29669–29686 (2018).

Thyreau, B., Sato, K., Fukuda, H. & Taki, Y. Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing. Med. Image Anal. 43, 214–228 (2018).

Ataloglou, D., Dimou, A., Zarpalas, D. & Daras, P. Fast and precise hippocampus segmentation through deep convolutional neural network ensembles and transfer learning. Neuroinformatics 17(4), 563–582 (2019).

Shi, Y., Cheng, K. & Liu, Z. Hippocampal subfields segmentation in brain MR images using generative adversarial networks. BioMed. Eng. OnLine 18, 5 (2019).

Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional networks for biomedical image segmentation. MICCAI 3(2015), 234–241 (2015).

Hancan, Z. et al. TITLE=dilated dense U-Net for infant hippocampus subfield segmentation. Front. Neuroinform. 13(30), 1–12 (2019).

Manjón, J. V. et al. Non-local MRI upsampling. Med. Image Anal. 14(6), 784–792 (2010).

Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L. & Robles, M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J. Magn. Reson. Imaging 31, 192–203 (2010).

Tustison, N. J. et al. N4ITK: Improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010).

Avants, B. B., Tustison, N. & Song, G. Advanced normalization tools (ANTS). Insight J. 2, 1–35 (2009).

Dou, Q. et al. 3D deeply supervised network for automated segmentation of volumetric medical images. Med. Image Anal. 41, 40–54 (2017).

Milletari, F., Navab, N., & Ahmadi, S. A. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Arxiv (2016).

Sudre, C. H., Li, W., Vercauteren, T., Ourselin, S. & Jorge, C. M. Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2017, ML-CDS 2017. Lecture Notes in Computer Science Vol. 10553 (eds Cardoso, M. et al.) (Springer, 2017).

Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D. mixup: Beyond Empirical Risk Minimization. https;//arXiv.org/abs/1710.09412 (2017)

Zijdenbos, A. P., Dawant, B. M., Margolin, R. A. & Palme, A. C. Morphometric analysis of white matter lesions in MR images: Method and validation. IEE Trans. Med. Imaging 13, 716–724 (1994).

Diederik, P. K. & Jimmy, L. B. Adam: A method for stochastic optimization. https;//arXiv.org/abs/1412.6980v9 (2014).

Eaton-Rosen, Z., Bragman, F., Ourselin, S. & Cardoso, M. J. Improving data augmentation for medical image segmentation. In International Conference on Medical Imaging with Deep Learning, MIDL2018 (2018).

Chen, Y., Xie, Y., Zhou, Z., Shi, G., Christodoulou, A. G. & Li, D. Brain MRI super resolution using 3D deep densely connected neural networks. In IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, 739–742 (2018).

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem