Deep neural networks for anatomical brain segmentation

ImageNet classification with deep convolutional neural networks

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Concussion classification via deep learning using whole-brain white matter fiber strains

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Federated Learning in Smart Healthcare: A Survey of Applications, Challenges, and Future Directions

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Removing segmentation inconsistencies with semi-supervised non-adjacency constraint

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Distributed deep learning across multisite datasets for generalized CT hemorrhage segmentation

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ICAM-Reg: Interpretable Classification and Regression With Feature Attribution for Mapping Neurological Phenotypes in Individual Scans

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Improved Noise2Noise Denoising with Limited Data

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Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images Using a Convolutional Neural Network Scheme

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Synergizing medical imaging and radiotherapy with deep learning

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Combining analysis of multi-parametric MR images into a convolutional neural network: precise target delineation for vestibular schwannoma treatment planning

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Automatic three-dimensional cephalometric annotation system using three-dimensional convolutional neural networks: a developmental trial

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Novel approach to classify brain tumor based on transfer learning and deep learning

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An AI-based segmentation and analysis pipeline for high-field MR monitoring of cerebral organoids

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Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

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3D-Brain Segmentation Using Deep Neural Network and Gaussian Mixture Model

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Deep learning-Based 3D inpainting of brain MR images

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A Survey of Deep Learning Techniques for Medical Diagnosis

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VertXNet: an ensemble method for vertebral body segmentation and identification from cervical and lumbar spinal X-rays

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Accurate and robust segmentation of neuroanatomy in T1‐weighted MRI by combining spatial priors with deep convolutional neural networks

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Multi-view Semi-supervised 3D Whole Brain Segmentation with a Self-ensemble Network

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Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data

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Using a Patch-Wise M-Net Convolutional Neural Network for Tissue Segmentation in Brain MRI Images

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Deep learning for brain disorders: from data processing to disease treatment

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Robust brain ROI segmentation by deformation regression and deformable shape model

Wu, Z., Guo, Y., Park, S. H., Gao, Y., Dong, P., Lee, S., & Shen, D. G. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.11.001 · Cited by 32

DeepBrainSeg: Automated Brain Region Segmentation for Micro-Optical Images With a Convolutional Neural Network

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Recent Advances in Deep Learning Techniques and Its Applications: An Overview

Hazra, A., Choudhary, P., & Singh, M. S. (2020) · Lecture notes in bioengineering · doi:10.1007/978-981-15-6329-4_10 · Cited by 33

Foveal fully convolutional nets for multi-organ segmentation

Brosch, T., & Saalbach, A. (2018) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2293528 · Cited by 30

Applications of interpretable deep learning in neuroimaging: A comprehensive review

Munroe, L., da Silva, M., Heidari, F., Grigorescu, I., Dahan, S., Robinson, E. C., Deprez, M., & So, P. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00214 · Cited by 33

Automated segmentation of 3D anatomical structures on CT images by using a deep convolutional network based on end-to-end learning approach

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Automatic Lesion Detection in Periapical X-rays

Sajad, M., Shafi, I., & Ahmad, J. (2019) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce47252.2019.8940661 · Cited by 28

Patch-Based U-Net Model for Isotropic Quantitative Differential Phase Contrast Imaging

Li, A.-C., Vyas, S., Lin, Y.-H., Huang, Y., Huang, H., & Luo, Y. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3091207 · Cited by 28

DBSegment: Fast and robust segmentation of deep brain structures considering domain generalization

Baniasadi, M., Petersen, M. S., Gonçalves, J. M., Horn, A., Vlasov, V., Hertel, F., & Husch, A. (2022) · Human Brain Mapping · doi:10.1002/hbm.26097 · Cited by 31

Development of accurate human head models for personalized electromagnetic dosimetry using deep learning

Rashed, E. A., Gomez-Tames, J. D., & Hirata, A. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116132 · Cited by 31

ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution MappingPreprint

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Correlated Regression Feature Learning for Automated Right Ventricle Segmentation

Chen, J., Zhang, H., Zhang, W., Du, X., Zhang, Y., & Li, S. (2018) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2018.2804947 · Cited by 27

3-D Quantum-Inspired Self-Supervised Tensor Network for Volumetric Segmentation of Medical Images

Konar, D., Bhattacharyya, S., Gandhi, T. K., Panigrahi, B. K., & Jiang, R. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3240238 · Cited by 27

Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

Wang, X., Zeng, W., Yang, X., Zhang, Y.-S., Fang, C., Zeng, S., Han, Y., & Fei, P. (2021) · eLife · doi:10.7554/elife.63455 · Cited by 30

MpMsCFMA-Net: Multi-path Multi-scale Context Feature Mixup and Aggregation Network for medical image segmentation

Che, M., Wu, Z., Zhang, J., Liu, X., Zhang, S., Liu, Y., Feng, S., & Wu, Y. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108292 · Cited by 26

A Systematic Review of Deep Learning Methods Applied to Ocular Images

Perdómo, O., & Gonzalez, F. A. (2019) · Ciencia e Ingeniería Neogranadina · doi:10.18359/rcin.4242 · Cited by 25

3D hemisphere-based convolutional neural network for whole-brain MRI segmentation

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Technical note: Evaluation of a V‐Net autosegmentation algorithm for pediatric CT scans: Performance, generalizability, and application to patient‐specific CT dosimetry

Adamson, P. M., Bhattbhatt, V., Principi, S., Beriwal, S., Strain, L. S., Offe, M., Wang, A., Vo, N., Schmidt, T. G., & Jordan, P. (2022) · Medical Physics · doi:10.1002/mp.15521 · Cited by 24

Unsupervised Domain Adaptation of MRI Skull-stripping Trained on Adult Data to Newborns

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TPUAR-Net: Two Parallel U-Net with Asymmetric Residual-Based Deep Convolutional Neural Network for Brain Tumor Segmentation

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AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation

Pierrick Coupé, A., Mansencal, B., Clément, M., Giraud, R., de Senneville, B. D., Ta, V., Lepetit, V., & Manjón, J. V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-32248-9_52 · Cited by 22

Shortcomings of Ventricle Segmentation Using Deep Convolutional Networks

Shao, M., Han, S., Carass, A., Li, X., Blitz, A. M., Prince, J. L., & Ellingsen, L. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-02628-8_9 · Cited by 21

3D Hyper-Dense Connected Convolutional Neural Network for Brain Tumor Segmentation

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Multi-Atlas Brain Parcellation Using Squeeze-and-Excitation Fully Convolutional Networks

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Patch-Based Deep Convolutional Neural Network for Corneal Ulcer Area Segmentation

Sun, Q.-C., Deng, L., Liu, J., Huang, H., Yuan, J., & Tang, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67561-9_11 · Cited by 20

Lung Nodule Segmentation Based on Convolutional Neural Networks Using Multi-orientation and Patchwise Mechanisms

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Classification of diseases from CT images using LSTM-based CNN

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Silver standard masks for data augmentation applied to deep-learning-based skull-stripping

Lucena, O., de Souza, R. M., Rittner, L., Frayne, R., & Lotufo, R. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363766 · Cited by 20

Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks

Li, H., Parikh, N. A., Wang, J., Merhar, S. L., Chen, M., Parikh, M. N., Holland, S. K., & He, L. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00610 · Cited by 19

Automatic segmentation and location learning of neonatal cerebral ventricles in 3D ultrasound data combining CNN and CPPN

Martin, M., Sciolla, B., Sdika, M., Quétin, P., & Delachartre, P. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104268 · Cited by 18

Enhanced Spatial Fuzzy C-Means Algorithm for Brain Tissue Segmentation in T1 Images

Jafrasteh, B., Lubián-Gutiérrez, M., Lubián‐López, S. P., & Benavente‐Fernández, I. (2024) · Neuroinformatics · doi:10.1007/s12021-024-09661-x · Cited by 17

Few-Shot Microscopy Image Cell Segmentation

Dawoud, Y., Hornauer, J., Carneiro, G., & Belagiannis, V. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-67670-4_9 · Cited by 17

Brain volumes quantification from MRI in healthy controls: Assessing correlation, agreement and robustness of a convolutional neural network-based software against FreeSurfer, CAT12 and FSL

Chaves, H., Dorr, F., Costa, M. E., Serra, M., Slezak, D. F., Farez, M., Sevlever, G. E., Yáñez, P., & Cejas, C. P. (2020) · Journal of Neuroradiology · doi:10.1016/j.neurad.2020.10.001 · Cited by 19

BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis

Lai, Z., Oliveira, L. C., Guo, R., Xu, W., Hu, Z., Mifflin, K., DeCarli, C. S., Cheung, S.-C. S., Chuah, C., & Dugger, B. N. (2022) · IEEE Access · doi:10.1109/access.2022.3171927 · Cited by 17

Label fusion method based on sparse patch representation for the brain MRI image segmentation

Liu, H., Yan, M., Song, E., Qian, Y., Xu, X., Jin, R., Jin, L., & Hung, C. (2017) · IET Image Processing · doi:10.1049/iet-ipr.2016.0988 · Cited by 17

Parcellation of visual cortex on high-resolution histological brain sections using convolutional neural networks

Spitzer, H., Amunts, K., Harmeling, S., & Dickscheid, T. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950666 · Cited by 17

MhURI:A Supervised Segmentation Approach to Leverage Salient Brain Tissues in Magnetic Resonance Images

Ghosal, P., Chowdhury, T., Kumar, A., Bhadra, A. K., Chakraborty, J., & Nandi, D. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105841 · Cited by 19

Generalizing deep learning brain segmentation for skull removal and intracranial measurements

Liu, Y., Huo, Y., Dewey, B. E., Wei, Y. J., Lyu, I., & Landman, B. A. (2022) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2022.01.004 · Cited by 17

Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks

Majdi, M. S., Keerthivasan, M., Rutt, B. K., Zahr, N. M., Rodrı́guez, J. J., & Saranathan, M. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.08.005 · Cited by 16

AES-CSFS: an automatic evaluation system for corneal sodium fluorescein staining based on deep learning

Wang, S., He, J., He, X., Liu, Y., Lin, X., Xu, C., Zhu, L., Kang, J., Wang, Y., Li, Y., et al. (2023) · Therapeutic Advances in Chronic Disease · doi:10.1177/20406223221148266 · Cited by 16

A Convolutional Neural Network for Skin Lesion Segmentation Using Double U-Net Architecture

Abid, I., Almakdi, S., Rahman, H., Almulihi, A. H., Alqahtani, A., Rajab, K. D., Alqhatani, A., & Shaikh, A. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.023753 · Cited by 16

An Ensemble of Deep Learning Object Detection Models for Anatomical and Pathological Regions in Brain MRI

Terzi, R. (2023) · Diagnostics · doi:10.3390/diagnostics13081494 · Cited by 16

Solution to overcome the sparsity issue of annotated data in medical domain

Pujitha, A. K., & Sivaswamy, J. (2018) · CAAI Transactions on Intelligence Technology · doi:10.1049/trit.2018.1010 · Cited by 15

Segmentation of carotid arterial walls using neural networks

Samber, D. D., Ramachandran, S., Sahota, A., Naidu, S., Pruzan, A. N., Fayad, Z. A., & Mani, V. (2019) · World Journal of Radiology · doi:10.4329/wjr.v12.i1.1 · Cited by 15

A 3D Fully Convolutional Neural Network With Top-Down Attention-Guided Refinement for Accurate and Robust Automatic Segmentation of Amygdala and Its Subnuclei

Liu, Y., Nacewicz, B. M., Zhao, G., Adluru, N., Kirk, G. R., Ferrazzano, P. A., Styner, M. A., & Alexander, A. L. (2020) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.00260 · Cited by 25

Evaluation of 3D and 2D Deep Learning Techniques for Semantic Segmentation in CT Scans

Shivdeo, A., Lokwani, R., Kulkarni, V., Kharat, A. T., & Pant, A. (2021) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd51485.2021.9519338 · Cited by 20

Distributed deep learning for robust multi-site segmentation of CT imaging after traumatic brain injury

Remedios, S. W., Roy, S., Blaber, J. A., Bermudez, C. L., Nath, V., Patel, M. B., Butman, J. A., Landman, B. A., & Pham, D. L. (2019) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2511997 · Cited by 15

A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT ImagesPreprint

Laradji, I., Rodríguez, P., Branchaud-Charron, F., Lensink, K., Atighehchian, P., Parker, W., Vázquez, D., & Nowrouzezahrai, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.07012 · Cited by 17

Artificial Intelligence in Biomedical Image Processing

Selvaraj, J., Prabha, T., Kumar, T. D. A., & Palaniappan, S. (2022) · Springer eBooks · doi:10.1007/978-981-16-5993-5_8 · Cited by 14

Reproducibility evaluation of SLANT whole brain segmentation across clinical magnetic resonance imaging protocols

Xiong, Y., Huo, Y., Wang, J., Davis, L. T., McHugo, M., & Landman, B. A. (2019) · SPIE Medical Imaging: Image Processing · doi:10.1117/12.2512561 · Cited by 14

Classical Statistics and Statistical Learning in Imaging Neuroscience

Bzdok, D. (2017) · Frontiers in Neuroscience · doi:10.3389/fnins.2017.00543 · Cited by 158

A fast stochastic framework for automatic MR brain images segmentation

Ismail, M., Soliman, A., Ghazal, M. A., Switala, A. E., Gimel’farb, G., Barnes, G. N., Khalil, A., & El-Baz, A. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0187391 · Cited by 13

Design of hybrid neural networks of the ensemble structure

Sineglazov, V. M., & Kot, A. T. (2021) · Eastern-European Journal of Enterprise Technologies · doi:10.15587/1729-4061.2021.225301 · Cited by 14

Traffic sign integrity analysis using deep learning

Acilo, J. N., Cruz, A. G. S. D., Kaw, M. K. L., Mabanta, M. D., Pineda, V. G. G., & Roxas, E. A. (2018) · IEEE International Colloquium on Signal Processing & Its Applications (CSPA) · doi:10.1109/cspa.2018.8368695 · Cited by 12

Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network

Ang, S. P., Phung, S. L., Schira, M. M., Bouzerdoum, A., & Duong, S. T. M. (2018) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta.2018.8615850 · Cited by 12

A Systematic Approach for Deep Learning Based Brain Tumor Segmentation

Sille, R., Choudhury, T., Chauhan, P., & Sharma, D. (2021) · Ingénierie des systèmes d information · doi:10.18280/isi.260301 · Cited by 12

YOLOv8-Based Frameworks for Liver and Tumor Segmentation Task on LiTS

Randar, S., Shah, V., Kulkarni, H., Suryawanshi, Y., Joshi, A. D., & Sawant, S. T. (2024) · SN Computer Science · doi:10.1007/s42979-024-03097-5 · Cited by 11

Organ Location Determination and Contour Sparse Representation for Multiorgan Segmentation

Li, S., Jiang, H., Yao, Y., & Yang, B. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2705037 · Cited by 11

Brain Tissue Segmentation via Deep Convolutional Neural Networks

Bikku, T., Karthik, J., Rao, G. R. K., Sree, K. P. N. V. S., Srinivas, P. V. V. S., & Prasad, C. (2021) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac52330.2021.9640635 · Cited by 11

Assessment of encoder-decoder-based segmentation models for thyroid ultrasound images

Yadav, N., Dass, R., & Virmani, J. (2023) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-023-02849-4 · Cited by 11

Brain White Matter Lesion Segmentation with 2D/3D CNN

Zorrilla, A. L., de Velasco-Vázquez, M., Serradilla, O., Roa-Barco, L., Graña, M., Chyzhyk, D., & Price, C. C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59740-9_39 · Cited by 11

Highway Network Block with Gates Constraints for Training Very Deep Networks

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