Medical Image Computing and Computer-Assisted Intervention — MICCAI’98: First International Conference Cambridge, MA, USA, October 11–13, 1998 Proceedings

A fast diffeomorphic image registration algorithm

Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122

Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration

Klein, A., Andersson, J., Ardekani, B. A., Ashburner, J., Avants, B., Chiang, M.-C., Christensen, G. E., Collins, D. L., Gee, J., Hellier, P., et al. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.037 · Cited by 2,227

Histopathological Image Analysis: A Review

Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., & Yener, B. (2009) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2009.2034865 · Cited by 2,052

HomER: A review of time-series analysis methods for near-infrared spectroscopy of the brain

Huppert, T. J., Diamond, S. G., Franceschini, M. A., & Boas, D. A. (2009) · Applied Optics · doi:10.1364/ao.48.00d280 · Cited by 1,817

Deformable medical image registration: a survey

Sotiras, A., Davatzikos, C., & Paragios, N. (2013) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2013.2265603 · Cited by 1,587

Hierarchical Feature Representation and Multimodal Fusion with Deep Learning for AD/MCI Diagnosis

Suk, H.-I., Lee, S.-W., & Shen, D. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.06.077 · Cited by 968

Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy

Aljabar, P., Heckemann, R. A., Hammers, A., Hajnal, J. V., & Rueckert, D. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.02.018 · Cited by 868

Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI

MacDonald, D., Kabani, N., Avis, D., & Evans, A. C. (2000) · NeuroImage · doi:10.1006/nimg.1999.0534 · Cited by 850

MIND: Modality independent neighbourhood descriptor for multi-modal deformable registration

Heinrich, M. P., Jenkinson, M., Bhushan, M., Matin, T., Gleeson, F. V., Brady, S. M., & Schnabel, J. A. (2012) · Medical Image Analysis · doi:10.1016/j.media.2012.05.008 · Cited by 707

Evidence for two types of brown adipose tissue in humans

Lidell, M. E., Betz, M. J., Leinhard, O. D., Heglind, M., Elander, L., Slawik, M., Mussack, T., Nilsson, D., Romu, T., Nuutila, P., et al. (2013) · Nature Medicine · doi:10.1038/nm.3017 · Cited by 657

White matter characterization with diffusional kurtosis imaging

Fieremans, E., Jensen, J. H., & Helpern, J. A. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.06.006 · Cited by 561

Diffeomorphic registration using geodesic shooting and Gauss–Newton optimisation

Ashburner, J., & Friston, K. J. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.049 · Cited by 503

Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

Fan, Y., Batmanghelich, N., Clark, C. M., & Davatzikos, C. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2007.10.031 · Cited by 477

Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index

Eelbode, T., Bertels, J., Berman, M., Vandermeulen, D., Maes, F., Bisschops, R., & Blaschko, M. B. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3002417 · Cited by 420

Fast and robust multi-atlas segmentation of brain magnetic resonance images

Lötjönen, J. M., Wolz, R., Koikkalainen, J. R., Thurfjell, L., Waldemar, G., Soininen, H., & Rueckert, D. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.026 · Cited by 391

Content-based image retrieval in radiology: current status and future directions

Akgül, C. B., Rubin, D. L., Napel, S., Beaulieu, C. F., Greenspan, H., & Acar, B. (2011) · Journal of Digital Imaging · doi:10.1007/s10278-010-9290-9 · Cited by 388

Variational Methods for Multimodal Image Matching

Hermosillo, G., Chefd'Hotel, C., & Faugeras, O. (2002) · International Journal of Computer Vision · doi:10.1023/a:1020830525823 · Cited by 388

Geodesic Shooting for Computational Anatomy

Miller, M. I., Trouvé, A., & Younes, L. (2006) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-005-3624-0 · Cited by 366

A deep learning framework for supporting the classification of breast lesions in ultrasound images

Han, S., Kang, H.-K., Jeong, J.-Y., Park, M.-H., Kim, W., Bang, W.-C., & Seong, Y.-K. (2017) · Physics in Medicine & Biology · doi:10.1088/1361-6560/aa82ec · Cited by 358

White matter lesion extension to automatic brain tissue segmentation on MRI

de Boer, R., Vrooman, H. A., van der Lijn, F., Vernooij, M. W., Ikram, M. A., van der Lugt, A., Breteler, M. M. B., & Niessen, W. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.01.011 · Cited by 329

Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks

Wolterink, J. M., Leiner, T., de Vos, B. D., van Hamersvelt, R. W., Viergever, M. A., & Išgum, I. (2016) · Medical Image Analysis · doi:10.1016/j.media.2016.04.004 · Cited by 321

Lesion identification using unified segmentation-normalisation models and fuzzy clustering

Seghier, M. L., Ramlackhansingh, A., Crinion, J., Leff, A. P., & Price, C. J. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.03.028 · Cited by 317

Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy

Bernasconi, N., Duchesne, S., Janke, A., Lerch, J., Collins, D. L., & Bernasconi, A. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.06.015 · Cited by 309

Multiresolution Markov models for signal and image processing

Willsky, A. S. (2002) · Proceedings of the IEEE · doi:10.1109/jproc.2002.800717 · Cited by 305

Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images

Ourselin, S., Roche, A., Prima, S., & Ayache, N. (2000) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-40899-4_57 · Cited by 279

Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images

Nayak, J., Bhat, P. S., Acharya U, R., Lim, C. M., & Kagathi, M. (2008) · Journal of Medical Systems · doi:10.1007/s10916-007-9113-9 · Cited by 272

Altered spontaneous activity in Alzheimer's disease and mild cognitive impairment revealed by Regional Homogeneity

Zhang, Z., Liu, Y., Jiang, T., Zhou, B., An, N., Dai, H., Wang, P., Niu, Y., Wang, L., & Zhang, X. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.049 · Cited by 255

Tractometer: Towards validation of tractography pipelines

Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M. (2013) · Medical Image Analysis · doi:10.1016/j.media.2013.03.009 · Cited by 228

Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation

Wang, H., & Yushkevich, P. A. (2013) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2013.00027 · Cited by 218

Tract-based morphometry for white matter group analysis

O'Donnell, L. J., Westin, C.-F., & Golby, A. J. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.12.023 · Cited by 186

Generative embedding for model-based classification of fMRI data

Brodersen, K. H., Schofield, T. M., Leff, A. P., Ong, C. S., Lomakina, E. I., Buhmann, J. M., & Stephan, K. E. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002079 · Cited by 165

Where2Act: From Pixels to Actions for Articulated 3D Objects

Mo, K., Guibas, L., Mukadam, M., Gupta, A., & Tulsiani, S. (2021) · arXiv · doi:10.48550/arxiv.2101.02692 · Cited by 132

Random Walks for Interactive Organ Segmentation in Two and Three Dimensions: Implementation and Validation

Grady, L., Schiwietz, T., Aharon, S., & Westermann, R. (2005) · Lecture Notes in Computer Science · doi:10.1007/11566489_95 · Cited by 113

GIST: an interactive, GPU-based level set segmentation tool for 3D medical images

CATES, J., LEFOHN, A., & WHITAKER, R. (2004) · Medical Image Analysis · doi:10.1016/j.media.2004.06.022 · Cited by 102

Reduced Gyral Window and Corpus Callosum Size in Autism: Possible Macroscopic Correlates of a Minicolumnopathy

Casanova, M. F., El-Baz, A., Mott, M., Mannheim, G., Hassan, H., Fahmi, R., Giedd, J., Rumsey, J. M., Switala, A. E., & Farag, A. (2009) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-008-0681-4 · Cited by 90

Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation

Otake, Y., Wang, A. S., Webster Stayman, J., Uneri, A., Kleinszig, G., Vogt, S., Khanna, A. J., Gokaslan, Z. L., & Siewerdsen, J. H. (2013) · Physics in Medicine and Biology · doi:10.1088/0031-9155/58/23/8535 · Cited by 85

Lymph node detection and segmentation in chest CT data using discriminative learning and a spatial prior

Feulner, J., Kevin Zhou, S., Hammon, M., Hornegger, J., & Comaniciu, D. (2013) · Medical Image Analysis · doi:10.1016/j.media.2012.11.001 · Cited by 72

Artificial intelligence in glioma imaging: Challenges and advances

Jin, W., Fatehi, M., Abhishek, K., Mallya, M., Toyota, B., & Hamarneh, G. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab8131 · Cited by 55

Evaluation of optimization methods for intensity-based 2D-3D registration in x-ray guided interventions

van der Bom, I. M. J., Klein, S., Staring, M., Homan, R., Bartels, L. W., & Pluim, J. P. W. (2011) · SPIE Proceedings · doi:10.1117/12.877655 · Cited by 55

Mediastinal atlas creation from 3-D chest computed tomography images: Application to automated detection and station mapping of lymph nodes

Feuerstein, M., Glocker, B., Kitasaka, T., Nakamura, Y., Iwano, S., & Mori, K. (2012) · Medical Image Analysis · doi:10.1016/j.media.2011.05.005 · Cited by 43

Model-based feature construction for multivariate decoding

Brodersen, K. H., Haiss, F., Ong, C. S., Jung, F., Tittgemeyer, M., Buhmann, J. M., Weber, B., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.04.036 · Cited by 36

The prolyl-isomerase PIN1 is essential for nuclear Lamin-B structure and function and protects heterochromatin under mechanical stress

Napoletano, F., Ferrari Bravo, G., Voto, I. A. P., Santin, A., Celora, L., Campaner, E., Dezi, C., Bertossi, A., Valentino, E., Santorsola, M., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109694 · Cited by 36

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network

Diniz, P. H. B., Valente, T. L. A., Diniz, J. O. B., Silva, A. C., Gattass, M., Ventura, N., Muniz, B. C., & Gasparetto, E. L. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.04.011 · Cited by 34

Multi-object Segmentation of Head Bones

Kainmueller, D., Lamecker, H., Seim, H., & Zachow, S. (2009) · The MIDAS Journal · doi:10.54294/t2bzlq · Cited by 30

Bone enhancement in ultrasound using local spectrum variations for guiding percutaneous scaphoid fracture fixation procedures

Anas, E. M. A., Seitel, A., Rasoulian, A., John, P. S., Pichora, D., Darras, K., Wilson, D., Lessoway, V. A., Hacihaliloglu, I., Mousavi, P., et al. (2015) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-015-1181-6 · Cited by 27

Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences

Kushki, A., Cardy, R. E., Panahandeh, S., Malihi, M., Hammill, C., Brian, J., Iaboni, A., Taylor, M. J., Schachar, R., Crosbie, J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab142 · Cited by 25

ACE: the Advanced Cohort Engine for searching longitudinal patient records

Callahan, A., Polony, V., Posada, J. D., Banda, J. M., Gombar, S., & Shah, N. H. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab027 · Cited by 22

Evaluation of SWI in Children with Sickle Cell Disease

Winchell, A. M., Taylor, B. A., Song, R., Loeffler, R. B., Grundlehner, P., Hankins, J. S., Wang, W. C., Ogg, R. J., Hillenbrand, C. M., & Helton, K. J. (2014) · American Journal of Neuroradiology · doi:10.3174/ajnr.a3794 · Cited by 13

Discriminative segmentation-based evaluation through shape dissimilarity

Konukoglu, E., Glocker, B., Ye, D. H., Criminisi, A., & Pohl, K. M. (2012) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2012.2216281 · Cited by 14