A novel deep learning based hippocampus subfield segmentation method

[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 of great interest since it can show earlier pathological changes in the brain. However, segmentation of these subfields is very difficult due to their complex structure and for the need of high-resolution magnetic resonance images manually labeled. In this work, we present a novel pipeline for automatic hippocampus subfield segmentation based on a deeply supervised convolutional neural network. Results of the proposed method are shown for two available hippocampus subfield delineation protocols. The method has been compared to other state-of-the-art methods showing improved results in terms of accuracy and execution time.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, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project) and Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57). The authors gratefully acknowledge the support of NVIDIA Corporation with their donation of the TITAN X GPU used in this research.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/s41598-022-05287-81912

HIPS: A new hippocampus subfield segmentation method

[EN] The importance of the hippocampus in the study of several neurodegenerative diseases such as Alzheimer's disease makes it a structure of great interest in neuroimaging. However, few segmentation methods have been proposed to measure its subfields due to its complex structure and the lack of high resolution magnetic resonance (MR) data. In this work, we present a new pipeline for automatic hippocampus subfield segmentation using two available hippocampus subfield delineation protocols that can work with both high and standard resolution data. The proposed method is based on multi-atlas label fusion technology that benefits from a novel multi-contrast patch match search process (using high resolution T1-weighted and T2-weighted images). The proposed method also includes as post-processing a new neural network-based error correction step to minimize systematic segmentation errors. The method has been evaluated on both high and standard resolution images and compared to other state-of-the-art methods showing better results in terms of accuracy and execution time.This research was supported by Spanish UPV2016-0099 and TIN2013-43457-R grants from UPV and the Ministerio de Economia y Competitividad. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project "Defi imag'In". We also want to thank Javier Juan Albarracin for his valuable contribution to the development of this method.Romero Gómez, JE.; Coupe, P.; Manjón Herrera, JV. (2017). HIPS: A new hippocampus subfield segmentation method. NeuroImage. 163:286-295. https://doi.org/10.1016/j.neuroimage.2017.09.049S28629516

Adaptive fusion of texture-based grading for Alzheimer's disease classification

[EN] Alzheimer's disease is a neurodegenerative process leading to irreversible mental dysfunctions. To date, diagnosis is established after incurable brain structure alterations. The development of new biomarkers is crucial to perform an early detection of this disease. With the recent improvement of magnetic resonance imaging, numerous methods were proposed to improve computer-aided detection. Among these methods, patch-based grading framework demonstrated state-of-the-art performance. Usually, methods based on this framework use intensity or grey matter maps. However, it has been shown that texture filters improve classification performance in many cases. The aim of this work is to improve performance of patch-based grading framework with the development of a novel texture-based grading method. In this paper, we study the potential of multi-directional texture maps extracted with 3D Gabor filters to improve patch-based grading method. We also proposed a novel patch-based fusion scheme to efficiently combine multiple grading maps. To validate our approach, we study the optimal set of filters and compare the proposed method with different fusion schemes. In addition, we also compare our new texture-based grading biomarker with state-of-the-art methods. Experiments show an improvement of AD detection and prediction accuracy. Moreover, our method obtains competitive performance with 91.3% of accuracy and 94.6% of area under a curve for AD detection. (C) 2018 Elsevier Ltd. All rights reserved.This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the Investments for the future Program IdEx Bordeaux (HL-MRI ANR-10-IDEX-03-02), Cluster of excellence CPU and TRAIL (BigDataBrain ANR-10-LABX-57).Hett, K.; Ta, V.; Manjón Herrera, JV.; Coupe, P. (2018). Adaptive fusion of texture-based grading for Alzheimer's disease classification. Computerized Medical Imaging and Graphics. 70:8-16. https://doi.org/10.1016/j.compmedimag.2018.08.002S8167

MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting

[EN] Accurate quantification of white matter hyperintensities (WMH) from Magnetic Resonance Imaging (MRI) is a valuable tool for the analysis of normal brain ageing or neurodegeneration. Reliable automatic extraction of WMH lesions is challenging due to their heterogeneous spatial occurrence, their small size and their diffuse nature. In this paper, we present an automatic method to segment these lesions based on an ensemble of overcomplete patch-based neural networks. The proposed method successfully provides accurate and regular segmentations due to its overcomplete nature while minimizing the segmentation error by using a boosted ensemble of neural networks. The proposed method compared favourably to state of the art techniques using two different neurodegenerative datasets. (C) 2018 Elsevier Ltd. All rights reserved.This research has been done thanks to the Australian distinguished visiting professor grant from the CSIRO (Commonwealth Scientific and Industrial Research Organisation) and the Spanish "Programa de apoyo a la investigacion y desarrollo (PAID-00-15)" of the Universidad Politecnica de Valencia. This research was partially supported by the Spanish grant TIN2013-43457-R from the Ministerio de Economia y competitividad. This study has been carried out also with support from the French State, managed by the French National Research Ageny in the frame of the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and TRAIL (HR-DTI ANR-10-LABX-57) and the CNRS multidisciplinary project Defi imag'In. Some of the data used in this work was collected by the AIBL study group. Funding for the AIBL study is provided by the CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF) in partnership with Edith Cowan University (ECU), Mental Health Research Institute (MHRI), Alzheimer's Australia (AA), National Ageing Research Institute (NARI), Austin Health, Macquarie University, CogState Ltd, Hollywood Private Hospital, and Sir Charles Gairdner Hospital.Manjón Herrera, JV.; Coupe, P.; Raniga, P.; Xia, Y.; Desmond, P.; Fripp, J.; Salvado, O. (2018). MRI white matter lesion segmentation using an ensemble of neural networks and overcomplete patch-based voting. Computerized Medical Imaging and Graphics. 69:43-51. https://doi.org/10.1016/j.compmedimag.2018.05.001S43516

Brain Commun.

The chronological progression of brain atrophy over decades, from pre-symptomatic to dementia stages, has never been formally depicted in Alzheimer's disease. This is mainly due to the lack of cohorts with long enough MRI follow-ups in cognitively unimpaired young participants at baseline. To describe a spatiotemporal atrophy staging of Alzheimer's disease at the whole-brain level, we built extrapolated lifetime volumetric models of healthy and Alzheimer's disease brain structures by combining multiple large-scale databases (n = 3512 quality controlled MRI from 9 cohorts of subjects covering the entire lifespan, including 415 MRI from ADNI1, ADNI2 and AIBL for Alzheimer's disease patients). Then, we validated dynamic models based on cross-sectional data using external longitudinal data. Finally, we assessed the sequential divergence between normal aging and Alzheimer's disease volumetric trajectories and described the following staging of brain atrophy progression in Alzheimer's disease: (i) hippocampus and amygdala; (ii) middle temporal gyrus; (iii) entorhinal cortex, parahippocampal cortex and other temporal areas; (iv) striatum and thalamus and (v) middle frontal, cingular, parietal, insular cortices and pallidum. We concluded that this MRI scheme of atrophy progression in Alzheimer's disease was close but did not entirely overlap with Braak staging of tauopathy, with a 'reverse chronology' between limbic and entorhinal stages. Alzheimer's disease structural progression may be associated with local tau accumulation but may also be related to axonal degeneration in remote sites and other limbic-predominant associated proteinopathies. © 2022 The Author(s). Published by Oxford University Press on behalf of the Guarantors of Brain.Apprentissage profond pour la volumétrie cérébrale : vers le BigData en neuroscienceTranslational Research and Advanced Imaging Laborator

Regional hippocampal vulnerability in early multiple sclerosis: a dynamic pathological spreading from dentate gyrus to CA1

"This is the peer reviewed version of the following article: Planche, V., Koubiyr, I., Romero, J. E., Manjon, J. V., Coupé, P., Deloire, M., ... & Tourdias, T. (2018). Regional hippocampal vulnerability in early multiple sclerosis: Dynamic pathological spreading from dentate gyrus to CA 1. Human brain mapping, 39(4), 1814-1824., which has been published in final form at https://doi.org/10.1002/hbm.23970. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Background: Whether hippocampal subfields are differentially vulnerable at the earliest stages of multiple sclerosis (MS) and how this impacts memory performance is a current topic of debate. Method: We prospectively included 56 persons with clinically isolated syndrome (CIS) suggestive of MS in a 1-year longitudinal study, together with 55 matched healthy controls at baseline. Participants were tested for memory performance and scanned with 3T MRI to assess the volume of 5 distinct hippocampal subfields using automatic segmentation techniques. Results: At baseline, CA4/dentate gyrus was the only hippocampal subfield with a volume significantly smaller than controls (p < .01). After one year, CA4/dentate gyrus atrophy worsened (-6.4%, p < .0001) and significant CA1 atrophy appeared (both in the stratum-pyramidale and the stratum radiatum-lacunosum-moleculare, -5.6%, p < .001 and -6.2%, p < .01, respectively). CA4/dentate gyrus volume at baseline predicted CA1 volume one year after CIS (R-2 = 0.44 to 0.47, p < .001, with age, T2 lesion-load, and global brain atrophy as covariates). The volume of CA4/dentate gyrus at baseline was associated with MS diagnosis during follow-up, independently of T2-lesion load and demographic variables (p < .05). Whereas CA4/dentate gyrus volume was not correlated with memory scores at baseline, CA1 atrophy was an independent correlate of episodic verbal memory performance one year after CIS (beta = 0.87, p < .05). Conclusion: The hippocampal degenerative process spread from dentate gyrus to CA1 at the earliest stage of MS. This dynamic vulnerability is associated with MS diagnosis after CIS and will ultimately impact hippocampal-dependent memory performance.ARSEP Foundation; Bordeaux University Hospital; TEVA Laboratories; French Agence Nationale de la Recherche, Grant/Award Numbers: ANR-10-LABX-57, ANR-10-LABX-43, ANR-10-IDEX-03-02, ANR-10-COHO-002; UPV, Grant/Award Numbers: UPV2016-0099, TIN2013-43457-R; Ministerio de Economia y competitividadPlanche, V.; Koubiyr, I.; Romero Gómez, JE.; Manjón Herrera, JV.; Coupe, P.; Deloire, M.; Dousset, V.... (2018). Regional hippocampal vulnerability in early multiple sclerosis: a dynamic pathological spreading from dentate gyrus to CA1. Human Brain Mapping. 39(4):1814-1824. https://doi.org/10.1002/hbm.23970S18141824394Avants, B. B., Tustison, N. J., Song, G., Cook, P. A., Klein, A., & Gee, J. C. (2011). A reproducible evaluation of ANTs similarity metric performance in brain image registration. NeuroImage, 54(3), 2033-2044. doi:10.1016/j.neuroimage.2010.09.025Bakker, A., Kirwan, C. B., Miller, M., & Stark, C. E. L. (2008). Pattern Separation in the Human Hippocampal CA3 and Dentate Gyrus. Science, 319(5870), 1640-1642. doi:10.1126/science.1152882Coupé, P., Manjón, J. V., Chamberland, M., Descoteaux, M., & Hiba, B. (2013). Collaborative patch-based super-resolution for diffusion-weighted images. NeuroImage, 83, 245-261. doi:10.1016/j.neuroimage.2013.06.030De Stefano, N., Airas, L., Grigoriadis, N., Mattle, H. P., O’Riordan, J., Oreja-Guevara, C., … Kieseier, B. C. (2014). Clinical Relevance of Brain Volume Measures in Multiple Sclerosis. CNS Drugs, 28(2), 147-156. doi:10.1007/s40263-014-0140-zDu, A. T., Schuff, N., Kramer, J. H., Ganzer, S., Zhu, X. P., Jagust, W. J., … Weiner, M. W. (2004). Higher atrophy rate of entorhinal cortex than hippocampus in AD. Neurology, 62(3), 422-427. doi:10.1212/01.wnl.0000106462.72282.90Dutta, R., Chang, A., Doud, M. K., Kidd, G. J., Ribaudo, M. V., Young, E. A., … Trapp, B. D. (2011). Demyelination causes synaptic alterations in hippocampi from multiple sclerosis patients. Annals of Neurology, 69(3), 445-454. doi:10.1002/ana.22337De Flores, R., La Joie, R., & Chételat, G. (2015). Structural imaging of hippocampal subfields in healthy aging and Alzheimer’s disease. Neuroscience, 309, 29-50. doi:10.1016/j.neuroscience.2015.08.033Fraser, M. A., Shaw, M. E., & Cherbuin, N. (2015). A systematic review and meta-analysis of longitudinal hippocampal atrophy in healthy human ageing. NeuroImage, 112, 364-374. doi:10.1016/j.neuroimage.2015.03.035Frisoni, G. B., Ganzola, R., Canu, E., Rub, U., Pizzini, F. B., Alessandrini, F., … Thompson, P. M. (2008). Mapping local hippocampal changes in Alzheimer’s disease and normal ageing with MRI at 3 Tesla. Brain, 131(12), 3266-3276. doi:10.1093/brain/awn280Gold, S. M., Kern, K. C., O’Connor, M.-F., Montag, M. J., Kim, A., Yoo, Y. S., … Sicotte, N. L. (2010). Smaller Cornu Ammonis 2–3/Dentate Gyrus Volumes and Elevated Cortisol in Multiple Sclerosis Patients with Depressive Symptoms. Biological Psychiatry, 68(6), 553-559. doi:10.1016/j.biopsych.2010.04.025Habbas, S., Santello, M., Becker, D., Stubbe, H., Zappia, G., Liaudet, N., … Volterra, A. (2015). Neuroinflammatory TNFα Impairs Memory via Astrocyte Signaling. Cell, 163(7), 1730-1741. doi:10.1016/j.cell.2015.11.023Hulst, H. E., Schoonheim, M. M., Van Geest, Q., Uitdehaag, B. M., Barkhof, F., & Geurts, J. J. (2015). Memory impairment in multiple sclerosis: Relevance of hippocampal activation and hippocampal connectivity. Multiple Sclerosis Journal, 21(13), 1705-1712. doi:10.1177/1352458514567727Jack, C. R., Petersen, R. C., Xu, Y., O’Brien, P. C., Smith, G. E., Ivnik, R. J., … Kokmen, E. (2000). Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology, 55(4), 484-490. doi:10.1212/wnl.55.4.484Jack, C. R., Barkhof, F., Bernstein, M. A., Cantillon, M., Cole, P. E., DeCarli, C., … Foster, N. L. (2011). Steps to standardization and validation of hippocampal volumetry as a biomarker in clinical trials and diagnostic criterion for Alzheimer’s disease. Alzheimer’s & Dementia, 7(4), 474-485.e4. doi:10.1016/j.jalz.2011.04.007Kerchner, G. A., Bernstein, J. D., Fenesy, M. C., Deutsch, G. K., Saranathan, M., Zeineh, M. M., & Rutt, B. K. (2013). Shared Vulnerability of Two Synaptically-Connected Medial Temporal Lobe Areas to Age and Cognitive Decline: A Seven Tesla Magnetic Resonance Imaging Study. Journal of Neuroscience, 33(42), 16666-16672. doi:10.1523/jneurosci.1915-13.2013La Joie, R., Fouquet, M., Mézenge, F., Landeau, B., Villain, N., Mevel, K., … Chételat, G. (2010). Differential effect of age on hippocampal subfields assessed using a new high-resolution 3T MR sequence. NeuroImage, 53(2), 506-514. doi:10.1016/j.neuroimage.2010.06.024Longoni, G., Rocca, M. A., Pagani, E., Riccitelli, G. C., Colombo, B., Rodegher, M., … Filippi, M. (2013). Deficits in memory and visuospatial learning correlate with regional hippocampal atrophy in MS. Brain Structure and Function, 220(1), 435-444. doi:10.1007/s00429-013-0665-9Manjón, J. V., & Coupé, P. (2016). volBrain: An Online MRI Brain Volumetry System. Frontiers in Neuroinformatics, 10. doi:10.3389/fninf.2016.00030Manjón, J. V., Coupé, P., Martí-Bonmatí, L., Collins, D. L., & Robles, M. (2009). Adaptive non-local means denoising of MR images with spatially varying noise levels. Journal of Magnetic Resonance Imaging, 31(1), 192-203. doi:10.1002/jmri.22003Manjón, J. V., Eskildsen, S. F., Coupé, P., Romero, J. E., Collins, D. L., & Robles, M. (2014). Nonlocal Intracranial Cavity Extraction. International Journal of Biomedical Imaging, 2014, 1-11. doi:10.1155/2014/820205Maruszak, A., & Thuret, S. (2014). Why looking at the whole hippocampus is not enough—a critical role for anteroposterior axis, subfield and activation analyses to enhance predictive value of hippocampal changes for Alzheimer’s disease diagnosis. Frontiers in Cellular Neuroscience, 8. doi:10.3389/fncel.2014.00095Miller, D. H., Chard, D. T., & Ciccarelli, O. (2012). Clinically isolated syndromes. The Lancet Neurology, 11(2), 157-169. doi:10.1016/s1474-4422(11)70274-5Morra, J. H., Tu, Z., Apostolova, L. G., Green, A. E., Avedissian, C., … Madsen, S. K. (2009). Automated 3D mapping of hippocampal atrophy and its clinical correlates in 400 subjects with Alzheimer’s disease, mild cognitive impairment, and elderly controls. Human Brain Mapping, 30(9), 2766-2788. doi:10.1002/hbm.20708Ny�l, L. G., & Udupa, J. K. (1999). On standardizing the MR image intensity scale. Magnetic Resonance in Medicine, 42(6), 1072-1081. doi:10.1002/(sici)1522-2594(199912)42:63.0.co;2-mPapadopoulos, D., Dukes, S., Patel, R., Nicholas, R., Vora, A., & Reynolds, R. (2009). Substantial Archaeocortical Atrophy and Neuronal Loss in Multiple Sclerosis. Brain Pathology, 19(2), 238-253. doi:10.1111/j.1750-3639.2008.00177.xPérez-Miralles, F., Sastre-Garriga, J., Tintoré, M., Arrambide, G., Nos, C., Perkal, H., … Montalban, X. (2013). Clinical impact of early brain atrophy in clinically isolated syndromes. Multiple Sclerosis Journal, 19(14), 1878-1886. doi:10.1177/1352458513488231Planche, V., Ruet, A., Coupé, P., Lamargue-Hamel, D., Deloire, M., Pereira, B., … Tourdias, T. (2016). Hippocampal microstructural damage correlates with memory impairment in clinically isolated syndrome suggestive of multiple sclerosis. Multiple Sclerosis Journal, 23(9), 1214-1224. doi:10.1177/1352458516675750Planche, V., Panatier, A., Hiba, B., Ducourneau, E.-G., Raffard, G., Dubourdieu, N., … Tourdias, T. (2017). Selective dentate gyrus disruption causes memory impairment at the early stage of experimental multiple sclerosis. Brain, Behavior, and Immunity, 60, 240-254. doi:10.1016/j.bbi.2016.11.010Planche, V., Ruet, A., Charré-Morin, J., Deloire, M., Brochet, B., & Tourdias, T. (2017). Pattern separation performance is decreased in patients with early multiple sclerosis. Brain and Behavior, 7(8), e00739. doi:10.1002/brb3.739Polman, C. H., Reingold, S. C., Banwell, B., Clanet, M., Cohen, J. A., Filippi, M., … Wolinsky, J. S. (2011). Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria. Annals of Neurology, 69(2), 292-302. doi:10.1002/ana.22366Rocca, M. A., Longoni, G., Pagani, E., Boffa, G., Colombo, B., Rodegher, M., … Filippi, M. (2015). In vivo evidence of hippocampal dentate gyrus expansion in multiple sclerosis. Human Brain Mapping, 36(11), 4702-4713. doi:10.1002/hbm.22946Romero, J. E., Coupe, P., & Manjón, J. V. (2016). High Resolution Hippocampus Subfield Segmentation Using Multispectral Multiatlas Patch-Based Label Fusion. Lecture Notes in Computer Science, 117-124. doi:10.1007/978-3-319-47118-1_15Romero, J. E., Coupé, P., & Manjón, J. V. (2017). HIPS: A new hippocampus subfield segmentation method. NeuroImage, 163, 286-295. doi:10.1016/j.neuroimage.2017.09.049Schmidt, P., Gaser, C., Arsic, M., Buck, D., Förschler, A., Berthele, A., … Mühlau, M. (2012). An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis. NeuroImage, 59(4), 3774-3783. doi:10.1016/j.neuroimage.2011.11.032Sicotte, N. L., Kern, K. C., Giesser, B. S., Arshanapalli, A., Schultz, A., Montag, M., … Bookheimer, S. Y. (2008). Regional hippocampal atrophy in multiple sclerosis. Brain, 131(4), 1134-1141. doi:10.1093/brain/awn030Small, S. A. (2014). Isolating Pathogenic Mechanisms Embedded within the Hippocampal Circuit through Regional Vulnerability. Neuron, 84(1), 32-39. doi:10.1016/j.neuron.2014.08.030Stark, S. M., Yassa, M. A., Lacy, J. W., & Stark, C. E. L. (2013). A task to assess behavioral pattern separation (BPS) in humans: Data from healthy aging and mild cognitive impairment. Neuropsychologia, 51(12), 2442-2449. doi:10.1016/j.neuropsychologia.2012.12.014Thompson, P. M., Hayashi, K. M., de Zubicaray, G. I., Janke, A. L., Rose, S. E., Semple, J., … Toga, A. W. (2004). Mapping hippocampal and ventricular change in Alzheimer disease. NeuroImage, 22(4), 1754-1766. doi:10.1016/j.neuroimage.2004.03.040Tustison, N. J., Avants, B. B., Cook, P. A., Yuanjie Zheng, Egan, A., Yushkevich, P. A., & Gee, J. C. (2010). N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29(6), 1310-1320. doi:10.1109/tmi.2010.2046908Wang, L., Swank, J. S., Glick, I. E., Gado, M. H., Miller, M. I., Morris, J. C., & Csernansky, J. G. (2003). Changes in hippocampal volume and shape across time distinguish dementia of the Alzheimer type from healthy aging☆. NeuroImage, 20(2), 667-682. doi:10.1016/s1053-8119(03)00361-6West, M. ., Coleman, P. ., Flood, D. ., & Troncoso, J. . (1994). Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. The Lancet, 344(8925), 769-772. doi:10.1016/s0140-6736(94)92338-8Winterburn, J. L., Pruessner, J. C., Chavez, S., Schira, M. M., Lobaugh, N. J., Voineskos, A. N., & Chakravarty, M. M. (2013). A novel in vivo atlas of human hippocampal subfields using high-resolution 3T magnetic resonance imaging. NeuroImage, 74, 254-265. doi:10.1016/j.neuroimage.2013.02.003Wisse, L. E. M., Daugherty, A. M., Olsen, R. K., Berron, D., Carr, V. A., … Stark, C. E. L. (2016). A harmonized segmentation protocol for hippocampal and parahippocampal subregions: Why do we need one and what are the key goals? Hippocampus, 27(1), 3-11. doi:10.1002/hipo.22671Yushkevich, P. A., Amaral, R. S. C., Augustinack, J. C., Bender, A. R., Bernstein, J. D., Boccardi, M., … Zeineh, M. M. (2015). 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. doi:10.1016/j.neuroimage.2015.01.00

Long-term antipsychotic and benzodiazepine use. and brain volume changes in schizophrenia : The Northern Finland Birth Cohort 1966 study

High doses of antipsychotics have been associated with loss in cortical and total gray matter in schizophrenia. However, previous imaging studies have not taken benzodiazepine use into account, in spite of evidence suggesting adverse effects such as cognitive impairment and increased mortality. In this Northern Finland Birth Cohort 1966 study, 69 controls and 38 individuals with schizophrenia underwent brain MRI at the ages of 34 and 43 years. At baseline, the average illness duration was over 10 years. Brain structures were delineated using an automated volumetry system, volBrain, and medication data on cumulative antipsychotic and benzodiazepine doses were collected using medical records and interviews. We used linear regression with intracranial volume and sex as covariates; illness severity was also taken into account. Though both medication doses associated to volumetric changes in subcortical structures, after adjusting for each other and the average PANSS total score, higher scan-interval antipsychotic dose associated only to volume increase in lateral ventricles and higher benzodiazepine dose associated with volume decrease in the caudate nucleus. To our knowledge, there are no previous studies reporting associations between benzodiazepine dose and brain structural changes. Further studies should focus on how these observations correspond to cognition and functioning.Peer reviewe

A CANDLE for a deeper in-vivo insight

A new Collaborative Approach for eNhanced Denoising under Low-light Excitation (CANDLE) is introduced for the processing of 3D laser scanning multiphoton microscopy images. CANDLE is designed to be robust for low signal-to-noise ratio (SNR) conditions typically encountered when imaging deep in scattering biological specimens. Based on an optimized non-local means filter involving the comparison of filtered patches, CANDLE locally adapts the amount of smoothing in order to deal with the noise inhomogeneity inherent to laser scanning fluorescence microscopy images. An extensive validation on synthetic data, images acquired on microspheres and in vivo images is presented. These experiments show that the CANDLE filter obtained competitive results compared to a state-of-the-art method and a locally adaptive optimized non-local means filter, especially under low SNR conditions (PSNR < 8 dB). Finally, the deeper imaging capabilities enabled by the proposed filter are demonstrated on deep tissue in vivo images of neurons and fine axonal processes in the Xenopus tadpole brain.We want to thank Florian Luisier for providing free plugin of his PureDenoise filter. We also want to thank Markku Makitalo for providing the code of their OVST. This study was supported by the Canadian Institutes of Health Research (CIHR, MOP-84360 to DLC and MOP-77567 to ESR) and Cda (CECR)-Gevas-OE016. MM holds a fellowship from the Deutscher Akademischer Austasch Dienst (DAAD) and a McGill Principal's Award. ESR is a tier 2 Canada Research Chair. This work has been partially supported by the Spanish Health Institute Carlos III through the RETICS Combiomed, RD07/0067/2001. This work benefited from the use of ImageJ.Coupé, P.; Munz, M.; Manjón Herrera, JV.; Ruthazer, ES.; Collins, DL. (2012). A CANDLE for a deeper in-vivo insight. Medical Image Analysis. 16(4):849-864. https://doi.org/10.1016/j.media.2012.01.002S84986416

AJNR Am J Neuroradiol

Accurate quantification of WM lesion load is essential for the care of patients with multiple sclerosis. We tested whether the combination of accelerated 3D-FLAIR and denoising using deep learning-based reconstruction could provide a relevant strategy while shortening the imaging examination. Twenty-eight patients with multiple sclerosis were prospectively examined using 4 implementations of 3D-FLAIR with decreasing scan times (4 minutes 54 seconds, 2 minutes 35 seconds, 1 minute 40 seconds, and 1 minute 15 seconds). Each FLAIR sequence was reconstructed without and with denoising using deep learning-based reconstruction, resulting in 8 FLAIR sequences per patient. Image quality was assessed with the Likert scale, apparent SNR, and contrast-to-noise ratio. Manual and automatic lesion segmentations, performed randomly and blindly, were quantitatively evaluated against ground truth using the absolute volume difference, true-positive rate, positive predictive value, Dice similarity coefficient, Hausdorff distance, and F1 score based on the lesion count. The Wilcoxon signed-rank test and 2-way ANOVA were performed. Both image-quality evaluation and the various metrics showed deterioration when the FLAIR scan time was accelerated. However, denoising using deep learning-based reconstruction significantly improved subjective image quality and quantitative performance metrics, particularly for manual segmentation. Overall, denoising using deep learning-based reconstruction helped to recover contours closer to those from the criterion standard and to capture individual lesions otherwise overlooked. The Dice similarity coefficient was equivalent between the 2-minutes-35-seconds-long FLAIR with denoising using deep learning-based reconstruction and the 4-minutes-54-seconds-long reference FLAIR sequence. Denoising using deep learning-based reconstruction helps to recognize multiple sclerosis lesions buried in the noise of accelerated FLAIR acquisitions, a possibly useful strategy to efficiently shorten the scan time in clinical practice.Translational Research and Advanced Imaging Laborator