Abstract
Volumetric quantification of tumors is usually done manually by radiologists requiring precious medical time and suffering from inter-observer variability. An automatic tool for accurate volume quantification of post-operative glioblastoma would reduce the workload of radiologists and improve the quality of follow-up monitoring and patient care. This paper deals with the 3-D segmentation of post-operative glioblastoma using channel squeeze and excitation based attention gated network (ASE-Net). The proposed deep neural network has a 3-D encoder and decoder based architecture with channel squeeze and excitation (CSE) blocks and attention blocks. The CSE block reduces the dependency on space information and put more emphasize on the channel information. The attention block suppresses the feature maps of irrelevant background and helps highlighting the relevant feature maps. The Uppsala university data set used has post-operative follow-up MRI scans for fifteen patients. A patient specific fine-tuning approach is used to improve the segmentation results for each patient. ASE-Net is also cross-validated with BraTS-2021 data set. The mean dice score of five-fold cross validation results with BraTS-2021 data set for enhanced tumor is 0.8244. The proposed network outperforms the competing networks like U-Net, Attention U-Net and Res U-Net. On the Uppsala University glioblastoma data set, the mean Dice score obtained with the proposed network is 0.7084, Hausdorff Distance-95 is 7.14 and the mean volumetric similarity achieved is 0.8579. With fine-tuning the pre-trained network, the mean dice score improved to 0.7368, Hausdorff Distance-95 decreased to 6.10 and volumetric similarity improved to 0.8736. ASE-Net outperforms the competing networks and can be used for volumetric quantification of post-operative glioblastoma from follow-up MRI scans. The network significantly reduces the probability of over segmentation.











Similar content being viewed by others
Data availability
The braTS-2021 dataset is public and can be downloaded from http://braintumorsegmentation.org/. The Uppsala University post-operative glioblastoma dataset is private and may be available upon request.
References
Kotowski K, Adamski S, Malara W, Machura B, Zarudzki L, Nalepa J. Segmenting brain tumors from MRI using cascaded 3D U-nets. Cham: Springer; 2021. p. 265–77. https://doi.org/10.1007/978-3-030-72087-2_23.
Dhara AK, Arvids E, Fahlström M, Wikström J, Larsson E-M, Strand R. Interactive segmentation of glioblastoma for post-surgical treatment follow-up. Twenty Fourth Int Conf Pattern Recognit (ICPR). 2018. https://doi.org/10.1109/ICPR.2018.8545105.
Goodenberger ML, Jenkins RB. Genetics of adult glioma. Cancer Genet. 2012;205(12):613–21. https://doi.org/10.1016/j.cancergen.2012.10.009.
Young RM, Jamshidi A, Davis G, Sherman JH. Current trends in the surgical management and treatment of adult glioblastoma. Ann Transl Med. 2015;3:9.
Gallego O. Nonsurgical treatment of recurrent glioblastoma. Curr Oncol. 2015;22(4):273–81.
Gering D, Sun K, Avery A, Chylla R, Vivekanandan A, Kohli L, Knapp H, Paschke B, Young-Moxon B, King N, Mackie T. Semi-automatic brain tumor segmentation by drawing long axes on multi-plane reformat: 4th international workshop, BrainLes 2018, held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018. Rev Sel Pap Part. 2019;II:441–55. https://doi.org/10.1007/978-3-030-11726-9_39.
Ilunga-Mbuyamba E, Cruz-Duarte JM, Avina-Cervantes JG, Correa-Cely CR, Lindner D, Chalopin C. Active contours driven by cuckoo search strategy for brain tumour images segmentation. Expert Syst Appl. 2016;56:59–68.
Li Y, Jia F, Qin J. Brain tumor segmentation from multimodal magnetic resonance images via sparse representation. Artif Intell Med. 2016;73:1–13.
Govindaraj V, Murugan PR, Subbaraj P, Vishnuvarthanan A. An unsupervised learning method with a clustering approach for tumor identification and tissue segmentation in magnetic resonance brain images. Appl Soft Comput. 2016;38:190–212.
Koley S, Sadhu A, Mitra P, Chakraborty B, Chakraborty C. Delineation and diagnosis of brain tumors from post contrast t1-weighted MR images using rough granular computing and random forest. Appl Soft Comput. 2016. https://doi.org/10.1016/j.asoc.2016.01.022.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Image Comput Comput. 9531:234–41. https://doi.org/10.1007/978-3-319-24574-4_28.
Oktay O, Schlemper J, Folgoc L, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla N, Kainz B, Glocker B, Rueckert D. Attention u-net: learning where to look for the pancreas. 2018.
Chen X, Yao L, Zhang Y. Residual attention u-net for automated multi-class segmentation of COVID-19 chest CT images. 2020.
Zhang Z, Liu Q. Road extraction by deep residual u-net. IEEE Geosci Remote Sens Lett. 2017. https://doi.org/10.1109/LGRS.2018.2802944.
Milletari F, Navab N, Ahmadi S-A. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), IEEE, 2016:565–571.
Ding Y, Chen F, Zhao Y, Wu Z, Zhang C, Wu D. A stacked multi-connection simple reducing net for brain tumor segmentation. IEEE Access. 2019. https://doi.org/10.1109/ACCESS.2019.2926448.
Hu K, Gan Q, Zhang Y, Deng S, Xiao F, Huang W, Cao C, Gao X. Brain tumor segmentation using multi-cascaded convolutional neural networks and conditional random field. IEEE Access. 2019;7:92615–29.
Deng W, Shi Q, Wang M, Zheng B, Ning N. Deep learning-based HCNN and CRF-RRNN model for brain tumor segmentation. IEEE Access. 2020;8:26665–75.
Razzak MI, Imran M, Xu G. Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inform. 2018;23(5):1911–9.
Zhang J, Xie Y, Wang Y, Xia Y. Inter-slice context residual learning for 3d medical image segmentation. IEEE Trans Med Imaging. 2020;40(2):661–72.
Liu P, Dou Q, Wang Q, Heng P-A. An encoder-decoder neural network with 3D squeeze-and-excitation and deep supervision for brain tumor segmentation. IEEE Access. 2020. https://doi.org/10.1109/ACCESS.2020.2973707.
Micallef N, Seychell D, Bajada CJ. Exploring the u-net++ model for automatic brain tumor segmentation. IEEE Access. 2021;9:125523–39. https://doi.org/10.1109/ACCESS.2021.3111131.
Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. Unet++: redesigning skip connections to exploit multiscale features in image segmentation. IEEE Trans Med Imaging. 2020;39(6):1856–67. https://doi.org/10.1109/TMI.2019.2959609.
Malmberg F, Strand R, Kullberg J, Nordenskjöld R, Bengtsson E. Smart paint: a new interactive segmentation method applied to MR prostate segmentation. In: MICCAI; 2012.
Dhara AK, Ayyalasomayajula KR, Arvids E, Fahlström M, Wikström J, Larsson E-M, Strand R. Segmentation of post-operative glioblastoma in MRI by u-net with patient-specific interactive refinement. In: Crimi A, Bakas S, Kuijf H, Keyvan F, Reyes M, van Walsum T, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer; 2019. p. 115–22.
Jansen MJ, Kuijf HJ, Dhara AK, Weaver NA, Biessels GJ, Strand R, Pluim JP. Patient-specific fine-tuning of convolutional neural networks for follow-up lesion quantification. J Med Imaging. 2020;7(6):064003.
Roy AG, Navab N, Wachinger C. Concurrent spatial and channel squeeze and excitation in fully convolutional networks. In: International conference on medical image computing and computer-assisted intervention. Cham: Springer; 2018. p. 421–9.
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7132–41.
Yang H, Shen Z, Li Z, Liu J, Xiao J. Combining global information with topological prior for brain tumor segmentation. In: Crimi A, Bakas S, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer; 2022. p. 204–15.
Li Z, Shen Z, Wen J, He T, Pan L. Automatic brain tumor segmentation using multi-scale features and attention mechanism. In: Crimi A, Bakas S, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer; 2022. p. 216–26.
Singh HS. Brain tumor segmentation using attention activated u-net with positive mining. In: Crimi A, Bakas S, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer; 2022. p. 431–40.
Jabareen N, Lukassen S. Segmenting brain tumors in multi-modal MRI scans using a 3D segnet architecture. In: Crimi A, Bakas S, editors. Brainlesion: glioma, multiple sclerosis, stroke and traumatic brain injuries. Cham: Springer; 2022. p. 377–88.
Ashtari P, Sima DM, De Lathauwer L, Sappey-Marinier D, Maes F, Van Huffel S. Factorizer: a scalable interpretable approach to context modeling for medical image segmentation. Med Image Anal. 2023;84:102706. https://doi.org/10.1016/j.media.2022.102706.
Raza R, Ijaz Bajwa U, Mehmood Y, Waqas Anwar M, Hassan Jamal M. dresu-net: 3d deep residual u-net based brain tumor segmentation from multimodal MRI. Biomed Signal Process Control. 2023;79:103861. https://doi.org/10.1016/j.bspc.2022.103861.
Wang W, Chen C, Ding M, Li J, Yu H, Zha S. Transbts: Multimodal brain tumor segmentation using transformer. In: International Conference on Medical Image Computing and Computer-Assisted Intervention 2021. https://api.semanticscholar.org/CorpusID:232147304
Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y. Transunet: Transformers make strong encoders for medical image segmentation. 2021. arXiv:abs/2102.04306
Acknowledgements
The authors would like to thank the Department of Biotechnology (DBT), Government of India (BT/PR41121/swdn/1357/020) and Vinnova, The Agency for Innovation Systems (2020-03616), Government of Sweden for supporting this work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Kundu, S., Banerjee, S., Breznik, E. et al. ASE-Net for Segmentation of Post-Operative Glioblastoma and Patient-Specific Fine-Tuning for Segmentation Refinement of Follow-Up MRI Scans. SN COMPUT. SCI. 5, 106 (2024). https://doi.org/10.1007/s42979-023-02425-5
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02425-5