MBANet: A 3D convolutional neural network with multi-branch attention for brain tumor segmentation from MRI images

Wang, 2020, Immunotherapy for malignant glioma: current status and future directions, Trends Pharmacol. Sci., 41, 123, 10.1016/j.tips.2019.12.003 Wadhwa, 2019, A review on brain tumor segmentation of MRI images, Magn. Reson. Imaging, 61, 247, 10.1016/j.mri.2019.05.043 Nan, 2022, Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions, Inform. Fusion, 82, 99, 10.1016/j.inffus.2022.01.001 Raschke, 2019, Tissue-type mapping of gliomas, NeuroImage: Clinical, 21, 101648, 10.1016/j.nicl.2018.101648 S. Dash, S. Verma, Kavita, S. Bevinakoppa, M. Woźniak, J. Shafi, M.F. Ijaz, Guidance image-based enhanced matched filter with modified thresholding for blood vessel extraction, Symmetry 14(2) (2022) 194. Wieczorek, 2021, Lightweight convolutional neural network model for human face detection in risk situations, IEEE Trans. Industr. Inform., 18, 4820, 10.1109/TII.2021.3129629 Dong, 2022, De-noising aggregation of graph neural networks by using principal component analysis, IEEE Trans. Industr. Inform. Woźniak, 2021, Deep neural network correlation learning mechanism for CT brain tumor detection, Neural Comput. Applic., 1 Zhang, 2021, MSMANet: A multi-scale mesh aggregation network for brain tumor segmentation, Appl. Soft Comput., 110, 10.1016/j.asoc.2021.107733 Chen, 2022, MTDCNet: A 3D multi-threading dilated convolutional network for brain tumor automatic segmentation, J. Biomed. Inform., 133, 10.1016/j.jbi.2022.104173 Shelhamer, 2017, Fully convolutional networks for semantic segmentation, IEEE Trans. Pattern Anal. Mach. Intell., 39, 640, 10.1109/TPAMI.2016.2572683 Ronneberger, 2015, 234 L. Chen, G. Papandreou, F. Schroff, H. Adam, Rethinking atrous convolution for semantic image segmentation, arXiv preprint arXiv:1706.05587 (2017). A. Howard, M. Sandler, G. Chu, L.C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, Q.V. Le, H. Adam, Searching for mobilemetv3, in: Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1314–1324. X. Zhang, X. Zhou, M. Lin, J. Sun, ShuffleNet: an extremely efficient convolutional neural network for mobile devices, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6848–6856. N. Ma, X. Zhang, H.T. Zheng, J. Sun, ShuffleNetV2: practical guidelines for efficient CNN architecture design, 2018, in: Proceedings of the European Conference on Computer Vision, 2018, pp. 116-131. K. Han, Y. Wang, Q. Tian, J. Guo, C. Xu, C. Xu, GhostNet: more features from cheap operations, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1580–1589. S. Mehta, M. Rastegari, L. Shapiro, H. Hajishirzi, ESPNetv2: a light-weight, power efficient, and general purpose convolutional neural network, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 9190–9200. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, Ł. Kaiser, I. Polosukhin, Attention is all you need, arXiv preprint arXiv:1706.03762 (2017). J. Park, S. Woo, J.Y. Lee, I.S. Kweon, BAM: bottleneck attention module, arXiv preprint arXiv:1807.06514 (2018). S. Woo, J. Park, J.Y. Lee, I.S. Kweon, CBAM: convolutional block attention module, in: Proceedings of the European Conference on Computer Vision, 2018, pp. 3–19. H. Li, P. Xiong, J. An, L. Wang, Pyramid attention network for semantic segmentation, arXiv preprint arXiv:1805.10180 (2018). Hu, 2018, Squeeze-and-Excitation networks, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132 Q. Zhang, Y. Yang, SA-Net: shuffle attention for deep convolutional neural networks, in: ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processin, 2021, pp. 2235–2239. Soltaninejad, 2017, Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI, Int. J. Comput. Assisted Radiol. Surg., 12, 183, 10.1007/s11548-016-1483-3 Soltaninejad, 2017, MRI brain tumor segmentation and patient survival prediction using random forests and fully convolutional networks Soltaninejad, 2018, Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels, Comput. Meth. Prog. Bio., 157, 69, 10.1016/j.cmpb.2018.01.003 Cinar, 2022, A hybrid DenseNet121-UNet model for brain tumor segmentation from MR Images, Biomed. Signal Process. Control, 76, 10.1016/j.bspc.2022.103647 Wu, 2022, MR brain segmentation based on DE-ResUNet combining texture features and background knowledge, Biomed. Signal Process. Control, 75, 10.1016/j.bspc.2022.103541 Chen, 2019, 184 Zhou, 2021, ERV-Net: an efficient 3D residual neural network for brain tumor segmentation, Expert Syst. Appl., 170, 10.1016/j.eswa.2021.114566 Liu, 2022, Scale-adaptive super-feature based MetricUNet for brain tumor segmentation, Biomed. Signal Process. Control, 73, 10.1016/j.bspc.2021.103442 Jiang, 2021, A novel deep learning model DDU-net using edge features to enhance brain tumor segmentation on MR images, Artif. Intell. Med., 121, 10.1016/j.artmed.2021.102180 Zhang, 2021, ME-Net: Multi-encoder net framework for brain tumor segmentation, Int. J. Imaging Syst. Techonl., 31, 1834, 10.1002/ima.22571 Abdollahi, 2020, VNet: An end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data, IEEE Access, 8, 179424, 10.1109/ACCESS.2020.3026658 Huang, 2021, A deep multi-task learning framework for brain tumor segmentation, Front. Oncol., 11 He, 2015, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell., 37, 1904, 10.1109/TPAMI.2015.2389824 Mehta, 2018, ESPNet: efficient spatial pyramid of dilated convolutions for semantic segmentation, 552 S. Mehta, M. Rastegari, L. Shapiro, H. Hajishirzi, ESPNetv2: a light-weight, power efficient, and general purpose convolutional neural network, in: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 9190–9200. Wang, 2021, DFP-ResUNet: convolutional neural network with a dilated convolutional feature pyramid for multimodal brain tumor segmentation, Comput. Methods Programs Biomed., 208, 10.1016/j.cmpb.2021.106208 Khan, 2021, PMED-Net: pyramid based multi-scale encoder-decoder network for medical image segmentation, IEEE Access, 9, 55988, 10.1109/ACCESS.2021.3071754 Zhou, 2020, AFPNet: a 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images, Neurocomputing, 402, 235, 10.1016/j.neucom.2020.03.097 Zhou, 2020, 3D dense connectivity network with atrous convolutional feature pyramid for brain tumor segmentation in magnetic resonance imaging of human heads, Comput. Biol. Med., 121, 10.1016/j.compbiomed.2020.103766 Kong, 2022, 3D hierarchical dual-attention fully convolutional networks with hybrid losses for diverse glioma segmentation, Knowl. Based Syst., 237, 10.1016/j.knosys.2021.107692 Huang, 2021, GCAUNet: a group cross-channel attention residual UNet for slice based brain tumor segmentation, Biomed. Signal Process. Control, 70, 10.1016/j.bspc.2021.102958 A.S. Akbar, C. Fatichah, N. Suciati, Single level UNet3D with multipath residual attention block for brain tumor segmentation, Journal of King Saud University – Computer and Information Sciences 34(6) 2022 3247-3258. Wang, 2021, Hybrid dilation and attention residual U-Net for medical image segmentation, Comput. Biol. Med., 134, 10.1016/j.compbiomed.2021.104449 Ma, 2021, Multi-task deep supervision on attention R2U-Net for brain tumor segmentation, Front. Oncol., 11, 10.3389/fonc.2021.704850 Zhou, 2020, Fusion based on attention mechanism and context constrain for multi-modal brain tumor segmentation, Comput. Med. Imaging Graph., 86, 10.1016/j.compmedimag.2020.101811 Xu, 2022, Brain tumor segmentation with corner attention and high-dimensional perceptual loss, Biomed. Signal Process. Control, 73, 10.1016/j.bspc.2021.103438 Guan, 2021, 3D AGSE-VNet: an automatic brain tumor MRI data segmentation framework, BMC Med, Imaging Goodfellow, 2022, Generative adversarial networks, Commun. ACM, 63, 139, 10.1145/3422622 Chen, 2021, JAS-GAN: Generative adversarial network based joint atrium and scar segmentations on unbalanced atrial targets, IEEE J. Biomed. Health, 26, 103, 10.1109/JBHI.2021.3077469 Wu, 2021, Fast and automated segmentation for the three-directional multi-slice cine myocardial velocity mapping, Diagnostics, 11, 346, 10.3390/diagnostics11020346 Jin, 2021, 3D PBV-Net: An automated prostate MRI data segmentation method, Comput. Biol. Med., 128, 10.1016/j.compbiomed.2020.104160 Liu, 2020, Exploring uncertainty measures in Bayesian deep attentive neural networks for prostate zonal segmentation, IEEE Access, 8, 151817, 10.1109/ACCESS.2020.3017168 Yang, 2020, Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention, Future Gener. Comp. Sy., 107, 215, 10.1016/j.future.2020.02.005 Liu, 2019, Automatic prostate zonal segmentation using fully convolutional network with feature pyramid attention, IEEE access, 7, 163626, 10.1109/ACCESS.2019.2952534 Q. Ye, J. Xia, G. Yang, Explainable AI for COVID-19 CT classifiers: An initial comparison study, in: the IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 2021, pp. 521–526. Yang, 2022, Unbox the black-box for the medical explainable AI via multi-modal and multi-centre data fusion: A mini-review, two showcases and beyond, Inform. Fusion, 77, 29, 10.1016/j.inffus.2021.07.016 Menze, 2015, The multimodal brain tumor image segmentation benchmark (BRATS), IEEE Trans. Med. Imag., 34, 1993, 10.1109/TMI.2014.2377694 Ketkar, 2021, Introduction to PyTorch Moshkov, 2020, Test-time augmentation for deep learning-based cell segmentation on microscopy images, Sci. Rep., 10, 5068, 10.1038/s41598-020-61808-3 S. Chandra, M. Vakalopoulou, L. Fidon, E. Battistella, T. Estienne, R. Sun, C. Robert, E. Deutch, N. Paragios, Context aware 3-d residual networks for brain tumor segmentation, in: International MICCAI Brainlesion Workshop, Springer, Cham, 2018, pp. 74–82. R. Hua, Q. Huo, Y. Gao, H. Sui, B. Zhang, Y. Sun, Z. Mo, F. Shi, Segmenting brain tumor using cascaded V-Nets in multimodal MR images, in: International MICCAI Brainlesion Workshop, Springer, Cham, 2018, pp. 49–60. Kermi, 2018, Deep convolutional neural networks using u-net for automatic brain tumor segmentation in multimodal MRI volumes, 37 Li, 2019, Multi-step cascaded networks for brain tumor segmentation, 163 Wang, 2019, 131 Islam, 2019, Brain tumor segmentation and survival prediction using 3D attention Unet, 262 Cheng, 2020, Effective and efficient multitask learning for brain tumor segmentation, J. Real-Time Image Pr., 17, 1951, 10.1007/s11554-020-00961-4 Kao, 2018, Brain tumor segmentation and tractographic feature extraction from structural MR images for overall survival prediction, 128 W. Chen, B. Liu, S. Peng, J. Sun, X. Qiao, S3D-UNet: separable 3D U-Net for brain tumor segmentation, in: International MICCAI Brainlesion Workshop, Springer, Cham, 2018, pp. 358–368. Zhou, 2022, A Tri-Attention fusion guided multi-modal segmentation network, Pattern Recogn., 124, 10.1016/j.patcog.2021.108417 Sheng, 2021, Second-order ResU-Net for automatic MRI brain tumor segmentation, Math. Biosci. Eng., 18, 4943, 10.3934/mbe.2021251 Xue, 2019, A multi-path decoder network for brain tumor segmentation, 255