Unsupervised Brain Anomaly Detection in MR Images
Many brain anomalies are associated with abnormal asymmetries. To detect and/or segment such anomalies in brain images, most automatic methods rely on supervised learning. This requires a large number of high-quality annotated training images, which is lacking for most medical image analysis problems. In contrast, unsupervised methods aim to learn a model from unlabeled healthy images, so that an unseen image that breaks priors of this model, i.e., an outlier, is considered an anomaly. This paper addresses the development of solutions to leverage unsupervised machine learning for the detection/analysis of abnormal brain asymmetries related to anomalies in magnetic resonance (MR) images. Experimental results on 3D MR-T1 images from healthy subjects and patients with a variety of lesions show the effectiveness and robustness of the proposed unsupervised approaches for brain anomaly detection.
Z. Akkus and et al., “Deep learning for brain MRI segmentation: state of the art and future directions,” Journal of Digital Imaging, vol. 30, no. 4, pp. 449–459, 2017.
M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.-M. Jodoin, and H. Larochelle, “Brain tumor segmentation with deep neural networks,” Medical Image Analysis, vol. 35, pp. 18–31, 2017.
B. Thyreau, K. Sato, H. Fukuda, and Y. Taki, “Segmentation of the hippocampus by transferring algorithmic knowledge for large cohort processing,” Medical Image Analysis, vol. 43, pp. 214–228, 2018.
C. Baur, B. Wiestler, S. Albarqouni, and N. Navab, “Deep autoencoding models for unsupervised anomaly segmentation in brain MR images,” in International MICCAI Brainlesion Workshop, 2018, pp. 161–169.
X. Chen, N. Pawlowski, M. Rajchl, B. Glocker, and E. Konukoglu, “Deep generative models in the real-world: An open challenge from medical imaging,” arXiv preprint arXiv:1806.05452, 2018.
D. Guo, J. Fridriksson, P. Fillmore, C. Rorden, H. Yu, K. Zheng, and S. Wang, “Automated lesion detection on MRI scans using combined unsupervised and supervised methods,” BMC Medical Imaging, vol. 15, no. 1, p. 50, 2015.
S. B. Martins, A. X. Falcão, and A. C. Telea, “BADRESC: Brain anomaly detection based on registration errors and supervoxel classification,” in International Joint Conference on Biomedical Engineering Systems and Technologies: BIOIMAGING, 2020, pp. 74–81, best student paper awards.
S. B. Martins, G. Ruppert, F. Reis, C. L. Yasuda, and A. X. Falcão, “A supervoxel-based approach for unsupervised abnormal asymmetry detection in MR images of the brain,” in IEEE International Symposium on Biomedical Imaging (ISBI), 2019, pp. 882–885.
S. B. Martins, A. C. Telea, and A. X. Falcão, “Extending supervoxelbased abnormal brain asymmetry detection to the native image space,” in IEEE Engineering in Medicine and Biology Society (EMBC), 2019, pp. 450–453.
D. Sato, S. Hanaoka, Y. Nomura, T. Takenaga, S. Miki, T. Yoshikawa, N. Hayashi, and O. Abe, “A primitive study on unsupervised anomaly detection with an autoencoder in emergency head ct volumes,” in SPIE Medical Imaging, 2018, p. 105751P.
S. B. Martins, “Unsupervised brain anomaly detection in MR images,” PhD thesis, University of Groningen, 2020.
N. J. Tustison, B. B. Avants, P. A. Cook, Y. Zheng, A. Egan, P. A. Yushkevich, and J. C. Gee, “N4ITK: improved N3 bias correction,” IEEE Transaction on Medical Imaging, vol. 29, no. 6, pp. 1310–1320, 2010.
V. S. Fonov, A. C. Evans, R. C. McKinstry, C. R. Almli, and D. L. Collins, “Unbiased nonlinear average age-appropriate brain templates from birth to adulthood,” Neuroimage, vol. 47, p. S102, 2009.
R. Phellan, A. X. Falcão, and J. K. Udupa, “Medical image segmentation via atlases and fuzzy object models: Improving efficacy through optimum object search and fewer models,” Medical Physics, vol. 43, no. 1, pp. 401–410, 2016.
J. E. Iglesias and M. R. Sabuncu, “Multi-atlas segmentation of biomedical images: a survey,” Medical Image Analysis, vol. 24, no. 1, pp. 205– 219, 2015.
S. B. Martins, J. Bragantini, C. L. Yasuda, and A. X. Falcão, “An adaptive probabilistic atlas for anomalous brain segmentation in MR images,” Medical Physics, vol. 46, no. 11, pp. 4940–4950, 2019.
C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995.
F. Malmberg, I. Nyström, A. Mehnert, C. Engstrom, and E. Bengtsson, “Relaxed image foresting transforms for interactive volume image segmentation,” in SPIE Medical Imaging, vol. 7623, 2010, p. 762340.
J. V. Manjón and P. Coupé, “volBrain: An online MRI brain volumetry system,” Frontiers in Neuroinformatics, vol. 10, 2016.
S. K. Warfield, K. H. Zou, and W. M. Wells, “Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation,” IEEE Transaction on Medical Imaging, vol. 23, no. 7, pp. 903–921, 2004.
S. B. Martins, B. C. Benato, B. F. Silva, C. L. Yasuda, and A. X. Falcão, “Modeling normal brain asymmetry in MR images applied to anomaly detection without segmentation and data annotation,” in SPIE Medical Imaging, vol. 10950, 2019, pp. 71–80.
J. Masci, U. Meier, D. Cires¸an, and J. Schmidhuber, “Stacked convolutional auto-encoders for hierarchical feature extraction,” in International Conference on Artificial Neural Networks, 2011, pp. 52–59.
L. M. Manevitz and M. Yousef, “One-class SVMs for document classification,” Journal of Machine Learning Research, vol. 2, no. Dec, pp. 139–154, 2001.
F. A. M. Cappabianco, A. X. Falcão, C. L. Yasuda, and J. K. Udupa, “Brain tissue MR-image segmentation via optimum-path forest clustering,” Computer Vision and Image Understanding, vol. 116, no. 10, pp. 1047–1059, 2012.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-SNE,” Journal of Machine Learning Research, vol. 9, pp. 2579–2605, Nov 2008.
P. E. Rauber, A. X. Falcão, and A. C. Telea, “Projections as visual aids for classification system design,” Information Visualization, vol. 17, no. 4, pp. 282–305, 2018.
S. B. Martins, A. C. Telea, and A. X. Falcão, “Investigating the impact of supervoxel segmentation for unsupervised abnormal brain asymmetry detection,” Computerized Medical Imaging and Graphics, vol. 85, p. 101770, 2020.
J. E. Vargas-Muñoz, A. S. Chowdhury, E. B. Alexandre, F. L. Galvão, P. A. V. Miranda, and A. X. Falcão, “An iterative spanning forest framework for superpixel segmentation,” IEEE Transaction on Image Processing, vol. 28, no. 7, pp. 3477–3489, 2019.
J. R. Taylor, N. Williams, R. Cusack, T. Auer, M. A. Shafto, M. Dixon, L. K. Tyler, R. N. Henson et al., “The cambridge centre for ageing and neuroscience (Cam-CAN) data repository: structural and functional mri, meg, and cognitive data from a cross-sectional adult lifespan sample,” Neuroimage, vol. 144, pp. 262–269, 2017.
S.-L. Liew and et al., “A large, open source dataset of stroke anatomical brain images and manual lesion segmentations,” Scientific Data, vol. 5, p. 180011, 2018.
A. X. Falcão, T. V. Spina, S. B. Martins, and R. Phellan, “Medical image segmentation using object shape models: A critical review on recent trends, and alternative directions,” Proc. of the Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE), pp. 9–15, 2015.
S. B. Martins, T. V. Spina, C. L. Yasuda, and A. X. Falcão, “A multiobject statistical atlas adaptive for deformable registration errors in anomalous medical image segmentation,” in SPIE Medical Imaging, vol. 10133, 2017, pp. 691–698.
S. B. Martins, A. X. Falcão, and A. C. Telea, “Combining registration errors and supervoxel classification for unsupervised brain anomaly detection,” in Biomedical Engineering Systems and Technologies. Springer International Publishing, 2021, pp. 140–164.
A. Z. Peixinho, S. B. Martins, J. E. Vargas, A. X. Falcão, J. F. Gomes, and C. T. N. Suzuki, “Diagnosis of human intestinal parasites by deep learning,” in Proc. of the Eccomas Thematic Conference on Computational Vision and Medical Image Processing (VipIMAGE), 2015, p. 107.
T. V. Spina, S. B. Martins, and A. X. Falcão, “Interactive medical image segmentation by statistical seed models,” in Conference on Graphics, Patterns and Images (SIBGRAPI), 2016, pp. 273–280.
S. B. Martins, G. Chiachia, and A. X. Falcão, “A fast and robust negative mining approach for enrollment in face recognition systems,” in Conference on Graphics, Patterns and Images (SIBGRAPI), 2017, pp. 201–208.
J. Bragantini, S. B. Martins, C. Castelo-Fernandez, and A. X. Falcão, “Graph-based image segmentation using dynamic trees,” in Iberoamerican Congress on Pattern Recognition (CIARP), 2018, pp. 470–478.
A. M. Sousa, S. B. Martins, A. X. Falcão, F. Reis, E. Bagatin, and K. Irion, “ALTIS: A fast and automatic lung and trachea CT-image segmentation method,” Medical Physics, vol. 46, no. 11, pp. 4970–4982, 2019.