mlsanityData to 7 Oct 2026

Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation

arXiv 2103.05529 ↗First posted 9 Mar 2021eess.IV cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

Clinical diagnostic and treatment decisions rely upon the integration of patient-specific data with clinical reasoning. Cancer presents a unique context that influence treatment decisions, given its diverse forms of disease evolution. Biomedical imaging allows noninvasive assessment of disease based on visual evaluations leading to better clinical outcome prediction and therapeutic planning. Early methods of brain cancer characterization predominantly relied upon statistical modeling of neuroimaging data. Driven by the breakthroughs in computer vision, deep learning became the de facto standard in the domain of medical imaging. Integrated statistical and deep learning methods have recently emerged as a new direction in the automation of the medical practice unifying multi-disciplinary knowledge in medicine, statistics, and artificial intelligence. In this study, we critically review major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation. The results do highlight that model-driven classical statistics and data-driven deep learning is a potent combination for developing automated systems in clinical oncology.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 54 of its 212 references that are in the map. First posted on arXiv 9 Mar 2021.

Builds on

54 of 212 references.

References · 54

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  5. 2014Going Deeper with Convolutionscited by 4,231
  6. 2013Auto-Encoding Variational Bayescited by 3,980
  7. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  8. 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
  9. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
  10. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  11. 2019An Introduction to Variational Autoencoderscited by 997
  12. 2016Training Deep Nets with Sublinear Memory Costcited by 825
38 more
  1. 2017Generalised Dice overlap as a deep learning loss function for highly unbalanced segmentationscited by 614
  2. 2018Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challengecited by 508
  3. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  4. 20183D MRI brain tumor segmentation using autoencoder regularizationcited by 324
  5. 2016Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentationcited by 263
  6. 2018Generative Adversarial Network in Medical Imaging: A Reviewcited by 257
  7. 2015Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understandingcited by 218
  8. 2015Conditional Random Fields as Recurrent Neural Networkscited by 213
  9. 2018Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Imagescited by 146
  10. 2020Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Studycited by 146
  11. 2015Brain Tumor Segmentation with Deep Neural Networkscited by 137
  12. 2016Unsupervised domain adaptation in brain lesion segmentation with adversarial networkscited by 125
  13. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  14. 2018Unsupervised Detection of Lesions in Brain MRI using constrained adversarial auto-encoderscited by 83
  15. 2019Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentationcited by 75
  16. 2016Semantic Segmentation using Adversarial Networkscited by 73
  17. 2018No New-Netcited by 61
  18. 2017Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentationcited by 58
  19. 2014A New 2.5D Representation for Lymph Node Detection using Random Sets of Deep Convolutional Neural Network Observationscited by 56
  20. 2018Brain Tumor Segmentation and Radiomics Survival Prediction: Contribution to the BRATS 2017 Challengecited by 56
  21. 2017SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentationcited by 49
  22. 2017CNN-based Segmentation of Medical Imaging Datacited by 48
  23. 2018Bayesian QuickNAT: Model Uncertainty in Deep Whole-Brain Segmentation for Structure-wise Quality Controlcited by 39
  24. 2017Reverse Classification Accuracy: Predicting Segmentation Performance in the Absence of Ground Truthcited by 35
  25. 2017A deep learning model integrating FCNNs and CRFs for brain tumor segmentationcited by 35
  26. 2018An application of cascaded 3D fully convolutional networks for medical image segmentationcited by 34
  27. 2017Automatic segmentation of MR brain images with a convolutional neural networkcited by 26
  28. 2018Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inferencecited by 25
  29. 2015Improving Computer-aided Detection using Convolutional Neural Networks and Random View Aggregationcited by 22
  30. 2018One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networkscited by 20
  31. 2019Unsupervised Deep Learning for Bayesian Brain MRI Segmentationcited by 20
  32. 2020A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosiscited by 17
  33. 2020PSIGAN: Joint probabilistic segmentation and image distribution matching for unpaired cross-modality adaptation based MRI segmentationcited by 14
  34. 2018Knowing what you know in brain segmentation using Bayesian deep neural networkscited by 13
  35. 2018Deep Generative Models in the Real-World: An Open Challenge from Medical Imagingcited by 12
  36. 2021Belief function-based semi-supervised learning for brain tumor segmentationcited by 11
  37. 2016Sub-cortical brain structure segmentation using F-CNN'scited by 10
  38. 2018A Novel Domain Adaptation Framework for Medical Image Segmentationcited by 9

50 most cited of 54 All 54 (JSON)

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024Biomedical Image Segmentation: A Systematic Literature Review of Deep Learning Based Object Detection Methodscited by 2
  2. 2025ReFRM3D: A Radiomics-enhanced Fused Residual Multiparametric 3D Network with Multi-Scale Feature Fusion for Glioma Characterizationcited by 0