U-Net architecture variants for brain tumor segmentation of histogram corrected images

[1] M. J. Ali, M. T. Akram, H. Saleem, B. Raza, A. R. Shahid, Glioma segmentation using ensemble of 2D/3D U-nets and survival prediction using multiple features fusion, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Comput. Sci., 12659 (2021), 189–199. ⇒7010.1007/978-3-030-72087-2_17 Search in Google Scholar

[2] U. Baid, S. Ghodasara, M. Bilello et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification, CoRR, abs/2107.02314, 2021. ⇒51 Search in Google Scholar

[3] V. Badrinarayanan, A. Kendall, R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation. CoRR, abs/1511.00561, 2015. ⇒56 Search in Google Scholar

[4] S. Bakas, M. Reyes, A. Jakab et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge, CoRR, abs/1811.02629, 2018. ⇒51 Search in Google Scholar

[5] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, J. Freymann, K. Farahani, C. Davatzikos, Segmentation labels for the pre-operative scans of the TCGA-GBM collection, 2017. ⇒52 Search in Google Scholar

[6] S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. Kirby, J. Freymann, K. Farahani, C. Davatzikos, Segmentation labels for the pre-operative scans of the TCGA-LGG collection, 2017. ⇒52 Search in Google Scholar

[7] V. L. Bommineni, Piecenet: A redundant Unet ensemble, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Comput. Sci, 12659 (2021), pp. 331–341. ⇒70 Search in Google Scholar

[8] A. Bäuerle, C. van Onzenoodt, T. Ropinski, Net2vis – a visual grammar for automatically generating publication-tailored CNN architecture visualizations, IEEE Transactions on Visualization and Computer Graphics, 27, 6,(2021) 2980–2991. ⇒59 Search in Google Scholar

[9] S. Chen, C. Ding, M. Liu, Dual-force convolutional neural networks for accurate brain tumor segmentation, Pattern Recognition, 88 (2019), 90–100. ⇒5110.1016/j.patcog.2018.11.009 Search in Google Scholar

[10] C.-B. S. Sang-Geun Choi, Detection of HGG and LGG brain tumors using UNet, Medico Legal Update, 19, 1, (2019) 560–565. ⇒50 Search in Google Scholar

[11] Y. Ding, C. Li, Q. Yang, Z. Qin, Z. Qin, How to improve the deep residual network to segment multi-modal brain tumor images, IEEE Access, 7, (2019) 152 821–152 831. ⇒51 Search in Google Scholar

[12] N. Gordillo, E. Montseny, P. Sobrevilla, State of the art survey on MRI brain tumor segmentation, Magnetic Resonance Imaging Journal, 31, 8, (2013) 1426–1438. ⇒51 Search in Google Scholar

[13] A. Győrfi, L. Szilágyi, L. Kovács, A fully automatic procedure for brain tumor segmentation from multi-spectral MRI records using ensemble learning and atlas-based data enhancement, Applied Sciences, 11, 2 (2021). ⇒5410.3390/app11020564 Search in Google Scholar

[14] K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, CoRR abs/1512.03385, 2015. ⇒59 Search in Google Scholar

[15] F. Isensee, P. F. Jäger, P. M. Full, P. Vollmuth, K. H. Maier-Hein, nnU-net for brain tumor segmentation, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lecture Notes in Comput. Sci., 12659 (2021), 118–132. ⇒51, 7010.1007/978-3-030-72087-2_11 Search in Google Scholar

[16] K. Kamnitsas, C. Ledig, V. F. Newcombe, J. P. Simpson, A. D. Kane, D. K. Menon, D. Rueckert, B. Glocker, Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation, Medical Image Analysis, 36, (2017) 61–78. ⇒51 Search in Google Scholar

[17] Kőble, A., Győrfi, Á., Csaholczi, S., Surányi, B., Dénes-Fazakas, L., Kovács, L., Szilágyi, L. Identifying the most suitable histogram normalization technique for machine learning based segmentation of multispectral brain MRI data. 2021 IEEE AFRICON, Arusha, Tanzania, 2021, pp. 71-76. ⇒5410.1109/AFRICON51333.2021.9570990 Search in Google Scholar

[18] L. Lefkovits, S. Lefkovits, L. Szilágyi, Brain tumor segmentation with optimized random forest, International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Lecture Notes in Comput. Sci. 10154 (2016), 88–99. ⇒5110.1007/978-3-319-55524-9_9 Search in Google Scholar

[19] H. McHugh, G. M. Talou, A. Wang, 2D dense-unet: A clinically valid approach to automated glioma segmentation, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Comput. Sci. 12659 (2021), 69–80. ⇒7010.1007/978-3-030-72087-2_7 Search in Google Scholar

[20] B. H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer et al. The multimodal brain tumor image segmentation benchmark (brats), IEEE Transactions on Medical Imaging, 34, 10 (2015) 1993–2024. ⇒51 Search in Google Scholar

[21] S. Pereira, A. Pinto, V. Alves, C. A. Silva, Brain tumor segmentation using convolutional neural networks in MRI images, IEEE Transactions on Medical Imaging, 35, 5 (2016) 1240–1251. ⇒5110.1109/TMI.2016.253846526960222 Search in Google Scholar

[22] O. Ronneberger, P. Fischer, T. Brox, U-Net: Convolutional networks for biomedical image segmentation, CoRR, abs/1505.04597, 2015. ⇒56, 57, 62 Search in Google Scholar

[23] C. Savadikar, R. Kulhalli, B. Garware, Brain tumour segmentation using probabilistic U-Net, Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Lecture Notes in Comput. Sci. 12659 (2021), pp. 255–264. ⇒ 7010.1007/978-3-030-72087-2_22 Search in Google Scholar

[24] E. Shelhamer, J. Long, T. Darrell, Fully convolutional networks for semantic segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 4, (2017) 640–651. ⇒5610.1109/TPAMI.2016.257268327244717 Search in Google Scholar

[25] K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, CoRR abs/1409.1556, 2015. ⇒58 Search in Google Scholar

[26] D. Wu, Y. Ding, M. Zhang, Q. Yang, Z. Qin, Multi-features refinement and aggregation for medical brain segmentation, IEEE Access, 8 (2020), 57 483–57 496. ⇒51 Search in Google Scholar

[27] Y. Xue, T. Xu, H. Zhang, L. R. Long, X. Huang, SegAN: Adversarial network with multi-scale L1 loss for medical image segmentation, Neuroinformatics, 16, 3-4, (2018), 383–392. ⇒51 Search in Google Scholar

[28] X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, Y. Fan, A deep learning model integrating FCNNs and CRFs for brain tumor segmentation, Medical Image Analysis, 43, (2018) 98–111. ⇒51 Search in Google Scholar

[29] * * * Brain Tumor Segmentation (BraTS) challenge, https://www.med.upenn.edu/cbica/brats2021/, 2021, online; accessed, April 2022. ⇒51 Search in Google Scholar

[30] * * * ImageNet,https://image-net.org, 2022, online; accessed, April 2022. ⇒ 58 Search in Google Scholar

[31] * * * Medical Segmentation Decathlon Challenge, http://medicaldecathlon.com/, 2021, online; accessed, April 2022. ⇒52 Search in Google Scholar