Journal of Shandong University(Engineering Science) ›› 2024, Vol. 54 ›› Issue (2): 80-89.doi: 10.6040/j.issn.1672-3961.0.2023.021

• Machine Learning & Data Mining • Previous Articles    

Glioma segmentation algorithm based on hybrid offset axial self-attention mechanism

GAO Zewen1,2, WANG Jian3, WEI Benzheng1,2*   

  1. 1. Medical Artificial Intelligence Research Center, Shandong University of Traditional Chinese Medicine, Jinan 266112, Shandong, China;
    2. Qingdao Academy of Chinese Medicine, Shandong University of Traditional Chinese Medicine, Qingdao 266112, Shandong, China;
    3. School of Science, Shandong Jiaotong University, Jinan 250357, Shandong, China
  • Published:2024-04-17

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  • TP18
[1] BEN K, HALL L O, GOLDGOF D B, et al. Ensembles of convolutional neural networks for survival time estimation of high-grade glioma patients from multimodal MRI[J]. Diagnostics, 2022, 12(2): 345-359.
[2] AHIR B K, ENGELHARD H H, LAKKA S S. Tumor development and angiogenesis in adult brain tumor:glioblastoma[J].Molecular Neurobiology, 2020, 57: 2461-2478.
[3] LI S, WANG C, CHEN J, et al. Signaling pathways in brain tumors and therapeutic interventions[J]. Signal Transduction and Targeted Therapy, 2023, 8(1): 8-31.
[4] BAI J, VARGHESE J, JAIN R. Adult glioma WHO classification update, genomics, and imaging: what the radiologists need to know[J]. Top Magn Reson Imaging, 2020, 29(2): 71-82.
[5] ANTONIO Di Ieva. Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario[J]. Neuroradiology, 2021, 63(8): 1-10.
[6] ANCA L, GROS U, WOLFGANG A, et al. PET for radiation treatment planning of brain tumours[J]. Radiotherapy and Oncology, 2010, 96(3): 325-327.
[7] CHU R, KIM G, TAUHID S, et al. Whole brain and deep gray matter atrophy detection over 5 years with 3T MRI in multiple sclerosis using a variety of automated segmentation pipelines[J]. PLoS ONE, 2018, 13(11): 1-14.
[8] TUSTISON N J, SHRINIDHI K L, WINTERMARK M, et al. Optimal symmetric multimodal templates and concatenated random forests for supervised brain tumor segmentation(simplified)with ANTsR[J]. Neuroinformatics, 2015, 13: 209-225.
[9] KAUS M, WARFIELD S, NABAVI A, et al. Automated segmentation of MR images of brain tumors [J]. Radiology, 2001, 218(2): 586-591.
[10] WU W, CHEN A, ZHAO L, et al. Brain tumor detection and segmentation in a CRF(conditional random fields)framework with pixel-pairwise affinity and superpixel-level features[J]. International Journal of Computer Assisted Radiology and Surgery, 2014, 9(2): 241-253.
[11] KHAN A, SOHAIL A, ZAHOORA U, et al. A survey of the recent architectures of deep convolutional neural networks[J]. Artificial Intelligence Review, 2020, 53(8): 5455-5516.
[12] MOHAMMED Y M A, EL S, JELLOULI I. A survey of methods for brain tumor segmentation-based MRI images[J]. Journal of Computational Design and Engineering, 2023, 10(1): 266-293.
[13] ZHU Z, HE X, QI G, et al. Brain tumor segmentation based on the fusion of deep semantics and edge information in multimodal MRI[J]. Information Fusion, 2023, 91: 376-387.
[14] HAVAEI M, DAVY A, WARDE-FARLEY D, et al.Brain tumor segmentation with deep neural networks[J].Medical Image Analysis, 2017, 35:18-31.
[15] ISLAM M, VIBASHAN V S, JOSE V J M, et al. Brain tumor segmentation and survival prediction using 3D attention UNet[C] //Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 5th International Workshop, BrainLes 2019, Heldin Conjunction with MICCAI 2019. Shenzhen, China. Springer, 2020: 262-272.
[16] LI X, CHEN H, QI X, et al. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes[J]. IEEE Transactions on Medical Imaging, 2018, 37(12): 2663-2674.
[17] MYRONENKO A. 3D MRI brain tumor segmentation using autoencoder regularization[C] // Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018. Granada, Spain: Springer, 2019: 311-320.
[18] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017: 30-40.
[19] DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: transformers for image recognition at scale[C] //International Conference on Learning Representations. Vienna, Austria: ICLR, 2021: 21-31.
[20] AZIZ M J,ZADEH M,FARNIA P, et al. Accurate automatic glioma segmentation in brain MRI images based on CapsNet[C] //International Conference of the IEEE Engineering in Medicine & Biology Society(EMBC). Guadalajara, Mexico: IEEE, 2021: 3882-3885.
[21] JIA Q, SHU H. Bitr-unet: a CNN-transformer combined network for MRI brain tumor segmentation[C] //International MICCAI Brainlesion Workshop. Singapore: Springer, 2022: 3-14.
[22] CHEN J, LU Y, YU Q, et al. Transunet: transformers make strong encoders for medical image segmentation[C] //International MICCAI Brainlesion Workshop. Strasbourg, France: Springer, 2021: 3-14.
[23] ZHANG Y, LIU H, HU Q. Transfuse: fusing transformers and cnns for medical image segmentation[C] //International Conference on Medical Image Computing and Computer-Assisted Intervention. Strasbourg, France: Springer, 2021: 14-24.
[24] WANG W, CHEN C, DING M, et al. Transbts: multimodal brain tumor segmentation using transformer[C] //International Conference on Medical Image Computing and Computer-Assisted Intervention. Strasbourg, France: Springer, 2021: 109-119.
[25] VALANARASU J M J, OZA P, HACIHALILOGLU I, et al. Medical transformer: gated axial-attention for medical image segmentation[C] //International Conference on Medical Image Computing and Computer-Assisted Intervention. Strasbourg, France:Springer, 2021: 36-46.
[26] QIN X, ZHANG Z, HUANG C, et al. U2-Net:going deeper with nested U-structure for salient object detection[J]. Pattern Recognition, 2020, 106: 107404.
[27] WANG H, ZHU Y, GREEN B, et al. Axial-deeplab: stand-alone axial-attention for panoptic segmentation[C] //European Conference on Computer Vision. Scotland, UK: Springer, 2020: 108-126.
[28] RAMACHANDRAN P, PARMAR N, VASWANI A, et al. Stand-alone self-attention in vision models[J]. Advances in Neural Information Processing Systems, 2019: 32-45.
[29] SHAW P, USZKOREIT J, VASWANI A. Self-attention with relative position representations[C] //Proceedings of NAACL-HLT. New Orleans, USA: 2018: 464-468.
[30] RAHMAN M A, WANG Y. Optimizing intersection-over-union in deep neural networks for image segmentation[C] //International Symposium on Visual Computing. Las Vegas, USA:Springer, 2016: 234-244.
[31] ABDOLLAHI A, PRADHAN B, ALAMRI A. VNet: an end-to-end fully convolutional neural network for road extraction from high-resolution remote sensing data[J]. IEEE Access, 2020, 8: 179424-179436.
[32] WANG Z, SIMONCELLI E P, BOVIK A C. Multiscale structural similarity for image quality assessment[C] //The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers. Pacific Grove, USA: IEEE, 2003: 1398-1402.
[33] DE BOER P T, KROESE D P, MANNOR S, et al. A tutorial on the cross-entropy method[J]. Annals of Operations Research, 2005, 134(1): 19-67.
[34] BAKAS S, AKBARI H, SOTIRAS A, et al. Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features[J]. Scientific Data, 2017, 4(1): 1-13.
[35] MENZE B H, JAKAB A, BAUER S, et al. The multimodal brain tumor image segmentation benchmark(BRATS)[J]. IEEE Transactions on Medical Imaging, 2014, 34(10): 1993-2024.
[36] ZHOU Z, RAHMAN S M M, TAJBAKHSH N, et al. Unet++: a nested U-net architecture for medical image segmentation[C] //International MICCAI Brainlesion Workshop. Granada, Spain:Springer, 2018: 3-11.
[37] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C] //International MICCAI Brainlesion Workshop. Munich, Germany: Springer, 2015: 234-241.
[38] HU Z, LI L, SUI A, et al. An efficient R-transformer network with dual encoders for brain glioma segmentation in MR images[J]. Biomedical Signal Processing and Control, 2023, 79: 104034.
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