Brain Extraction from Magnetic Resonance Images Using UNet modified with Residual and Dense Layers

Year 2023, , 144 - 151, 27.09.2023

Kali Gurkahraman , Çağrı Daşgın

https://doi.org/10.46810/tdfd.1339665

Cited By: 1

Abstract

The main goal of brain extraction is to separate the brain from non-brain parts, which enables accurate detection or classification of abnormalities within the brain region. The precise brain extraction process significantly influences the quality of successive neuroimaging analyses. Brain extraction is a challenging task mainly due to the similarity of intensity values between brain and non-brain structure. In this study, a UNet model improved with ResNet50 or DenseNet121 feature extraction layers was proposed for brain extraction from Magnetic Resonance Imaging (MRI) images. Three publicly available datasets (IBSR, NFBS and CC-359) were used for training the deep learning models. The findings of a comparison between different feature extraction layer types added to UNet shows that residual connections taken from ResNet50 is more successful across all datasets. The ResNet50 connections proved effective in enhancing the distinction of weak but significant gradient values in brain boundary regions. In addition, the best results were obtained for CC-359. The improvement achieved with CC-359 can be attributed to its larger number of samples with more slices, indicating that the model learned better. The performance of our proposed model, evaluated using test data, is found to be comparable to the results obtained in the literature.

Keywords

Brain extraction, Skull stripping, Deep learning, Dense connection, Residual connection, UNet.

References

• [1] Kalavathi P, Prasath VS. Methods on skull stripping of MRI head scan images-a review. Journal of Digital Imaging. 2016; 29: 365-379.
• [2] Isensee F, Schell M, Pflueger I, Brugnara G, Bonekamp D, Neuberger U, et al. Automated brain extraction of multisequence MRI using artificial neural networks. Human Brain Mapping. 2019; 40(17): 4952-4964, 2019.
• [3] Bhat SY, Naqshbandi A, Abulaish M. Skull stripping on multimodal brain MRI scans using thresholding and morphology. The Imaging Science Journal, 2023; 1-13.
• [4] Karakis R, Gurkahraman K, Mitsis GD, Boudrias MH. Deep learning prediction of motor performance in stroke individuals using neuroimaging data. Journal of Biomedical Informatics. 2023; 141: article number 104357.
• [5] Smith SM. Fast robust automated brain extraction. Human Brain Mapping. 2002; 17: 143-155.
• [6] Souza R, Lucena O, Garrafa J, Gobbi D, Saluzzi M, Appenzeller S, et al. An open, multi-vendor, multi-field-strength brain MR dataset and analysis of publicly available skull stripping methods agreement. NeuroImage. 2018; 170: 482-494.
• [7] Jenkinson M, Pechaud M, Smith S. BET2 - MR-based estimation of brain, skull and scalp surfaces. Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), Oxford, 2005.
• [8] Shattuck DW, Sandor-Leahy SR, Schaper KA, Rottenberg DA, Leahy RM. Magnetic resonance image tissue classification using a partial volume model. NeuroImage, 2001; 13 (5): 856-876.
• [9] Eskildsen SF, Coupe P, Fonov V, Manjon JV, Leung KK, Guizard N, et al. BEaST: brain extraction based on Non-local segmentation technique. NeuroImage. 2012; 59 (3): 2362-2373.
• [10] Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M., et al. A survey on deep learning in medical image analysis. Medical Image Analysis. 2017; 42: 60-88.
• [11] Yapici M, Karakis R, Gurkahraman K. Improving Brain Tumor Classification with Deep Learning Using Synthetic Data. Computers, Materials and Continua. 2023; 74 (3): 5049-5067.
• [12] Gurkahraman K, Karakis R. Brain tumors classification with deep learning using data augmentation. Journal of the Faculty of Engineering and Architecture of Gazi University. 2021; 36 (2): 997-1011.
• [13] Rehman HZU, Hwang H, Lee S. Conventional and deep learning methods for skull stripping in brain MRI. Applied Sciences. vol. 10, no. 5, article number 1773, 2020.
• [14] Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. Neuroimage. 2016; 129: 460-469.
• [15] Hwang H, Rehman HZU, Lee S. 3D U-Net for skull stripping in brain MRI. Applied Sciences. 2019; 9 (3): article number 569.
• [16] Zhang Q, Wang L, Zong X, Lin W, Li G, Shen D. FRNET: Flattened Residual Network for Infant MRI Skull Stripping. arXiv 2019; arXiv:1904.05578.
• [17] Daşgın Ç, Gürkahraman K. Artık Bağlantılar ile Düzenlenen U-Net Mimarisi Kullanarak Beyin Çıkarımı. International Conference on Applied Engineering and Natural Sciences ICAENS 2023. Konya, Turkey: 2023. p.348.
• [18] Hoopes A, Mora JS, Dalca AV, Fischl B, Hoffmann M. SynthStrip: Skull-stripping for any brain image. NeuroImage. 2022; 260: article number 119474.
• [19] IBSR [Internet]. The Internet Brain Segmentation Repository (IBSR) [cited 2023 July 27]. Available from: https://www.nitrc.org/projects/ibsr
• [20] Puccio B, Pooley JP, Pellman JS, Taverna EC, Craddock RC. The preprocessed connectomes project repository of manually corrected skull-stripped T1-weighted anatomical MRI data. GigaScience. 2016; 5: article number 45.
• [21] Kingma DP, Welling M. Auto-encoding variational bayes. 2013; arXiv preprint, arXiv:1312.6114.
• [22] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference. Munich, Germany: 2015, Proceedings, Part III 18, Springer International Publishing; 2015. p. 234-241.
• [23] Çiçek Ö, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2016: 19th International Conference. Athens, Greece: 2016, Proceedings, Part II 19, Springer International Publishing; 2016. p. 424-432.
• [24] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA; 2016. p. 770-778.
• [25] Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA; 2017. p. 4700-4708.
• [26] Montagnon E, Cerny M, Cadrin-Chênevert A, Hamilton V, Derennes T, Ilinca A, et al. Deep learning workflow in radiology: a primer. Insights into Imaging. 2020; 11: 1-15.
• [27] Rana M, Bhushan M. Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimedia Tools and Applications. 2023; 82(17): 26731-26769.
• [28] Iglesias JE, Liu CY, Thompson PM, Tu Z. Robust brain extraction across datasets and comparison with publicly available methods. IEEE Transactions on Medical Imaging, 2011; 30 (9): 1617-1634.