Medical image segmentation methods overview
DOI:
https://doi.org/10.20535/SRIT.2308-8893.2018.1.05Keywords:
medical image segmentation, multi-atlas based method, deep learning approachAbstract
This article provides an overview of the modern medical image segmentation methods. The most popular methods such as multi-atlas based methods and deep learning approach are considered in more details. In addition, this article overviews different steps of the multi-atlas based methods (MAS) in detail and shows which modern algorithms and approaches used in different steps of MAS to achieve state of the art results in the medical image segmentation task and how it affects the accuracy of the algorithm. Also, there is a brief description of the modern deep learning algorithms which are used for the medical image segmentation. Such type of algorithm is used as an independent algorithm or as a part of the MAS. Finally, this article summarizes described algorithms and evaluate which approaches promise to improve state of the art result of the medical image segmentation in the future.References
Wu Z. Segmenting hippocampal subfields from 3T MRI with multi-modality images / Z. Wu, Y. Gao, F. Shi et al. // Segmenting hippocampal subfields from 3T MRI with multi-modality images. — Vol. 43. — 2018. — P.10–22.
You W. Semi-automatic segmentation of the placenta into fetal and maternal compartments using intravoxel incoherent motion MRI / W. You, N. Andescavage, Z. Zun, C. Limperopoulos // Medical Imaging. — Vol. 10137. — 2017.
Oguz I. LOGISMOS: A Family of Graph-Based Optimal Image segmentation methods / I. Oguz, H. Bogunovic, S. Kashyap et al. // Medical Image Recognition, Segmentation and Parsing Machine Learning and Multiple Object Approaches. — Academic Press, 2016. — P.179–206.
Sanroma G. Multiple-atlas segmentation in medical imaging / G. Sanroma, G. Wu, M. Kim, M.A. González et al. // Medical Image Recognition, Segmentation and Parsing. — Academic Press, 2016. — P.231–257.
Carass A. An Overview of the Multi-Object Geometric Deformable Model Approach in Biomedical Imaging / A. Carass, J.L. Prince // Medical Image Recognition, Segmentation and Parsing Machine Learning and Multiple Object Approaches. — Academic Press, 2016. — P.259–279.
Zhou K. Deep Learning for Medical Image Analysis / K. Zhou, H. Greenspan, D. Shen. — Academic Press, 2017.
Criminisi A. Geos: Geodesic image segmentation / A. Criminisi, T. Sharp, A. Blake // Computer Vision — ECCV 2008. ECCV 2008. Lecture Notes in Computer Science. — Springer, 2008. — P.99–112.
Heiberg E. Design and validation of segment-freely available software for cardiovascular image analysis / E. Heiberg, J. Sjogren, M. Ugander et al. // BMC medical imaging. — Vol. 10. — 2010.
Pohl K. Bayesian model for joint segmentation and registration / K. Pohl, J. Fisher, L. Grimson et al. // Neuroimage. — Vol. 31. — 2006. — P. 228–239.
Yeo B. Effects of registration regularization and atlas sharpness on segmentation accuracy / B. Yeo, M. Sabuncu, R. Desikan et al. // Medical image analysis. — Vol. 12. — 2008. — P. 603–615.
Heckemann R. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion / R. Heckemann, J. Hajnal, P. Aljabar et al. // Neuroimage. — N. 33. — 2006. — P. 115–126.
Heiberg E. Design and validation of Segment – freely available software for cardiovascular image analysis / E. Heiberg, J. Sjögren, M. Ugander, M. Carlsson et al. // BMC Medical Imaging. — Vol. 10. — 2010. — P. 1.
Babalola K. An evaluation of four automatic methods of segmenting the subcortical structures in the brain / K. Babalola, B. Patenaude, P. Aljabar et al. // NeuroImage. — Vol. 47, N. 4. — 2009. — P. 1435–1447.
Langerak T. Label Fusion in Atlas-Based Segmentation Using a Selective and Iterative Method for Performance Level Estimation (SIMPLE) / T. Langerak, U. van der Heide, A. Kotte et al. // IEEE Transactions on Medical Imaging. — Vol. 29. — 2010. — P. 2000–2008.
Hao Y. Local label learning (LLL) for subcortical structure segmentation: Application to hippocampus segmentation / Y. Hao, T. Wang, X. Zhang et al. // Human Brain Mapping. — Vol. 35. — 2014. — P. 2674–2697.
Wolz R. LEAP: Learning embeddings for atlas propagation / R. Wolz, P. Aljabar, J. V. Hajnal et al., the Alzheimer's Disease Neuroimaging Initiative // NeuroImage. — Vol. 49, N. 2. — 2010. — P. 1316–1325.
Chakravarty M. Performing label-fusionbased segmentation using multiple automatically generated templates / M. Chakravarty, P. Steadman, M. Eede et al. // Human brain mapping. — Vol. 34. — 2013. — P. 2635–2654.
Yushkevich P. Nearly automatic segmentation of hippocampal subfields in in vivo focal T2-weighted MRI / P. Yushkevich, H. Wang, J. Pluta et al. // Neuroimage. — Vol. 53. — 2010. — P. 1208–1224.
Nouranian S. A multi-atlas-based segmentation framework for / S. Nouranian, S. Mahdavi, I. Spadinger et al. // IEEE Transactions on Medical Imaging. — Vol. 34. — 2015. — P. 950–961.
Wang L. Segmentation of neonatal brain MR images using patch-driven level sets / L. Wang, F. Shi, G. Li et al. // Neuroimage. — Vol. 84. — 2014. — P. 141–158.
Asman A. Out-of-atlas likelihood estimation using multi-atlas segmentation / A. Asman, L. Chambless, R. Thompson, B. Landman // Medical physics. — Vol. 40, N. 4. — 2013.
Studholme C. An overlap invariant entropy measure of 3D medical image alignment / C. Studholme, D.L. Hill, D. Hawkes // Pattern Recognition. — Vol. 32. — 1999. — P. 71–86.
Heckemann R. The mirror method of assessing segmentation quality in atlas label / R. Heckemann, A. Hammers, P. Aljabar et al. // Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on. — 2009. — P. 1194–1197.
Avants B. The optimal template effect in hippocampus studies of diseased populations / B. Avants, P. Yushkevich, J. Pluta et al. // NeuroImage. — Vol. 49, N. 3. — 2010. — P. 2457–2466.
Nouranian S. A Multi-Atlas-Based Segmentation Framework for Prostate Brachytherapy / S. Nouranian, S.S. Mahdavi, I. Spadinger et al. // IEEE Transactions on Medical Imaging. — Vol. 34, N. 4. — 2015. — P. 950–961.
Konukoglu E. Neighbourhood approximation using randomized forests / E. Konukoglu, B. Glocker, D. Zikic, A. Criminisi // Medical Image Analysis. — Vol. 17, N. 7. — 2013. — P. 790–804.
Liaw A. Classification and Regression by randomForest / A. Liaw, M. Wiener // R news. — Vol. 2, N. 3. — 2002. — P. 18–22.
Sanroma G. Learning to rank atlases for multiple-atlas segmentation / G. Sanroma, G. Wu, Y. Gao, D. Shen // IEEE Trans Med Imaging. — Vol. 33. — 2014. — P. 1939–1953.
Sotiras A. Deformable Medical Image Registration: A Survey / A. Sotiras, D. Christos, P. Nikos // IEEE Transactions on Medical Imaging. — Vol. 32. — 2013. — P. 1153–1190.
Ou Y. Comparative Evaluation of Registration Algorithms in Different Brain Databases With Varying Difficulty: Results and Insights / Y. Ou, H. Akbari, M. Bilello et al. // IEEE Transactions on Medical Imaging. — Vol. 33. — 2014. — P. 2039–2065.
Rohlfing T. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains / T. Rohlfing, R. Brandt, R. Menzel, Jr. CR Maurer // Neuroimage. — Vol. 21. — 2004. — P. 1428–1442.
Klein A. Automated brain labeling with multiple atlases / A. Klein, B. Mensh, S. Ghosh et al. // BMC Medical Imaging. — Vol. 5. — 2005. — P. 7.
Artaechevarria X. Efficient classifier generation and weighted voting for atlas-based segmentation: Two small steps faster and closer to the combination oracle / X. Artaechevarria, A. Muñoz-Barrutia, C. Ortiz-de Solorzano // Medical Imaging 2008: Image Processing. —2008.
Wan J. Automated reliable labeling of the cortical surface / J. Wan, A. Carass, S. Resnick, J. Prince // Biomedical Imaging: From Nano to Macro. — 2008. — P. 440–443.
Zhang D. Confidence-guided sequential label fusion for multi-atlas based / D. Zhang, G. Wu, H. Jia, D. Shen // MICCAI. — Vol. 6893. — 2011. — P. 643–650.
Rousseau F. A supervised patch-based approach for human brain labeling / F. Rousseau, P.A. Habas, C. Studholme // IEEE Transactions on Medical Imaging. — Vol. 30, N. 10. — 2011. — P. 1852–1862.
Wang H. Regression-based label fusion for multi-atlas segmentation / H. Wang, J. Suh, S. Das et al. // Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. — 2011. — P. 1113–1120.
Warfield S.K. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation / S.K. Warfield, K.H. Zou, W.M. Wells // IEEE Transactions on Medical Imaging. — Vol. 23. — 2004. — P. 903–921.
Asman A.J. Hierarchical performance estimation in the statistical label fusion framework / A.J. Asman, B.A. Landman // Medical Image Analysis. — Vol. 18, N. 7. — 2014. — P. 1070–1081.
Sabuncu M. A Generative Model for Image Segmentation Based on Label Fusion / M. Sabuncu, Y. Thomas, K. Leemput et al. // IEEE Transactions on Medical Imaging. — Vol. 29. — 2010. — P. 1714–1729.
Wang H. Multi-atlas segmentation with learning-based label fusion / H. Wang, Y. Cao, T. Syeda-Mahmood // Machine Learning in Medical Imaging. — 2014. — P. 256–263.
Bengio Y. Representation Learning: A Review and New Perspectives / Y. Bengio, A. Courville, P. Vincent // IEEE Transactions on Pattern Analysis and Machine Intelligence. — Vol. 35, N. 8. — 2013. — P. 1798–1828.
Shin Hoo-Chang Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data / Hoo-Chang Shin, Matthew R. Orton, David J. Collins, Simon J. Doran, Martin O. Leach // IEEE Transactions on Pattern Analysis and Machine Intelligence. — Vol. 35, N. 8. — 2013. — P. 1930–1943.
Krizhevsky A. ImageNet Classification with Deep Convolutional Neural Networks / A. Krizhevsky, I. Sutskever, G. Hinton // Advances in Neural Information Processing Systems 25. — Curran Associates, Inc., 2012. — P.1097–1105.
Ciresan D. Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images / D. Ciresan, A. Giusti, L. Gambardella, J. Schmidhuber // Advances in Neural Information Processing Systems 25. — Curran Associates, Inc., 2012. — P.2843–2851.
Guo Y. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching / Y. Guo, Y. Gao, D. Shen // Deep Learning for Medical Image Analysis. — Academic Press, 2017. — P.221–247.
Ronneberger O. U-Net: Convolutional Networks for Biomedical Image Segmentation / O. Ronneberger, P. Fischer, T. Brox // Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Lecture Notes in Computer Science. — Springer, 2015. — P.234–241.
Jia Y. Caffe: Convolutional architecture for fast feature embedding / Y. Jia, E. Shelhamer, J. Donahue et al. // MM '14 Proceedings of the 22nd ACM international conference on Multimedia. — 2014. — P. 675–678.
Milletari F. V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation / F. Milletari, N. Navab, Sed-Ahmad Ahmadi // 3D Vision (3DV), 2016 Fourth International Conference on. — 2016. — P. 565–571.
Litjens G. Evaluation of prostate segmentation algorithms for MRI: the PROMISE12 challenge / G. Litjens, R. Toth, W. van de Ven et al. // Medical image analysis. — Vol. 18. — 2014. — P. 1361–8423.
