To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size, location, structure, and grade of the tumor, automatic tumor segmentation in MRI images aids in a more thorough analysis of pathological conditions. Due to the intensity differences in MRI images, gliomas may spread out, have low contrast, and are therefore difficult to detect. As a result, segmenting brain tumors is a challenging process. In the past, several methods for segmenting brain tumors in MRI scans were created. However, because of their susceptibility to noise and distortions, the usefulness of these approaches is limited. Self-Supervised Wavele- based Attention Network (SSW-AN), a new attention module with adjustable self-supervised activation functions and dynamic weights, is what we suggest as a way to collect global context information. In particular, this network's input and labels are made up of four parameters produced by the two-dimensional (2D) Wavelet transform, which makes the training process simpler by neatly segmenting the data into low-frequency and high-frequency channels. To be more precise, we make use of the channel attention and spatial attention modules of the self-supervised attention block (SSAB). As a result, this method may more easily zero in on crucial underlying channels and spatial patterns. The suggested SSW-AN has been shown to outperform the current state-of-the-art algorithms in medical image segmentation tasks, with more accuracy, more promising dependability, and less unnecessary redundancy.

Department of Control Systems and Instrumentation Faculty of Mechanical Engineering VSB Technical University of Ostrava 17 Listopadu 2172 15 708 00 Ostrava Czech Republic

Department of R and D Bond Marine Consultancy London EC1V 2NX UK

Vellore Institute of Technology Chennai Campus Chennai 600127 India

Zobrazit více v PubMed

Li S., Zhang C., He X. Medical Image Computing and Computer Assisted Intervention—MICCAI 2020. Springer International Publishing; Cham, Switzerland: 2020. Shape-aware semi-supervised 3D semantic segmentation for medical images; pp. 552–561.

Jin K., Huang X., Zhou J., Li Y., Yan Y., Sun Y., Zhang Q., Wang Y., Ye J. FIVES: A fundus image dataset for artificial intelligence based vessel segmentation. Sci. Data. 2022;9:475. doi: 10.1038/s41597-022-01564-3. PubMed DOI PMC

Xie B., Li S., Li M., Liu C.H., Huang G., Wang G. SePiCo: Semantic-guided pixel contrast for domain adaptive semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2023:1–17. doi: 10.1109/TPAMI.2023.3237740. PubMed DOI

Asgari Taghanaki S., Abhishek K., Cohen J.P., Cohen-Adad J., Hamarneh G. Deep semantic segmentation of natural and medical images: A review. Artif. Intell. Rev. 2021;54:137–178. doi: 10.1007/s10462-020-09854-1. DOI

Liu L., Wu F.-X., Wang Y.-P., Wang J. Multi-receptive-field CNN for semantic segmentation of medical images. IEEE J. Biomed. Health Inform. 2020;24:3215–3225. doi: 10.1109/JBHI.2020.3016306. PubMed DOI

Ban Y., Wang Y., Liu S., Yang B., Liu M., Yin L., Zheng W. 2D/3D multimode medical image alignment based on spatial histograms. Appl. Sci. 2022;12:8261. doi: 10.3390/app12168261. DOI

Cheng Z., Qu A., He X. Contour-aware semantic segmentation network with spatial attention mechanism for medical image. Vis. Comput. 2022;38:749–762. doi: 10.1007/s00371-021-02075-9. PubMed DOI PMC

Lu H., Wang H., Zhang Q., Yoon S.W., Won D. A 3D convolutional neural network for volumetric image semantic segmentation. Procedia Manuf. 2019;39:422–428. doi: 10.1016/j.promfg.2020.01.386. DOI

Alalwan N., Abozeid A., ElHabshy A.A., Alzahrani A. Efficient 3D deep learning model for medical image semantic segmentation. Alex. Eng. J. 2021;60:1231–1239. doi: 10.1016/j.aej.2020.10.046. DOI

Fang K., Li W.-J. Medical Image Computing and Computer Assisted Intervention—MICCAI 2020. Springer International Publishing; Cham, Switzerland: 2020. DMNet: Difference minimization network for semi-supervised segmentation in medical images; pp. 532–541.

Rezaei M., Yang H., Harmuth K., Meinel C. Conditional generative adversarial refinement networks for unbalanced medical image semantic segmentation; Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV); Waikoloa Village, HI, USA. 7–11 January 2019.

Rezaei M., Yang H., Meinel C. Recurrent generative adversarial network for learning imbalanced medical image semantic segmentation. Multimed. Tools Appl. 2020;79:15329–15348. doi: 10.1007/s11042-019-7305-1. DOI

Jiang F., Grigorev A., Rho S., Tian Z., Fu Y., Jifara W., Adil K., Liu S. Medical image semantic segmentation based on deep learning. Neural Comput. Appl. 2018;29:1257–1265. doi: 10.1007/s00521-017-3158-6. DOI

Petit O., Thome N., Charnoz A., Hostettler A., Soler L. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Springer International Publishing; Cham, Switzerland: 2018. Handling missing annotations for semantic segmentation with deep ConvNets; pp. 20–28.

Karayegen G., Aksahin M.F. Brain tumor prediction on MR images with semantic segmentation by using deep learning network and 3D imaging of tumor region. Biomed. Signal Process. Control. 2021;66:102458. doi: 10.1016/j.bspc.2021.102458. DOI

Xia Y., Zhang Y., Liu F., Shen W., Yuille A.L. Computer Vision—ECCV 2020. Springer International Publishing; Cham, Switzerland: 2020. Synthesize then compare: Detecting failures and anomalies for semantic segmentation; pp. 145–161.

Wang Z., Zheng J.-Q., Voiculescu I. Medical Image Understanding and Analysis. Springer International Publishing; Cham, Switzerland: 2022. An uncertainty-aware transformer for MRI cardiac semantic segmentation via mean teachers; pp. 494–507.

van Rijthoven M., Balkenhol M., Siliņa K., van der Laak J., Ciompi F. HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images. Med. Image Anal. 2021;68:101890. doi: 10.1016/j.media.2020.101890. PubMed DOI

Yang X., Yu L., Li S., Wen H., Luo D., Bian C., Qin J., Ni D., Heng P.-A. Towards automated semantic segmentation in prenatal volumetric ultrasound. IEEE Trans. Med. Imaging. 2019;38:180–193. doi: 10.1109/TMI.2018.2858779. PubMed DOI

Ali M., Gilani S.O., Waris A., Zafar K., Jamil M. Brain tumour image segmentation using deep networks. IEEE Access. 2020;8:153589–153598. doi: 10.1109/ACCESS.2020.3018160. DOI

Kumar D.M., Satyanarayana D., Prasad M.N.G. An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation. Multimed. Tools Appl. 2021;80:6939–6957. doi: 10.1007/s11042-020-09635-6. DOI

Wang W., Chen C., Ding M., Yu H., Zha S., Li J. Medical Image Computing and Computer Assisted Intervention—MICCAI 2021. Springer International Publishing; Cham, Switzerland: 2021. TransBTS: Multimodal brain tumor segmentation using transformer; pp. 109–119.

Wadhwa A., Bhardwaj A., Singh Verma V. A review on brain tumor segmentation of MRI images. Magn. Reson. Imaging. 2019;61:247–259. doi: 10.1016/j.mri.2019.05.043. PubMed DOI

Zhao Y.-X., Zhang Y.-M., Liu C.-L. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing; Cham, Switzerland: 2020. Bag of tricks for 3D MRI brain tumor segmentation; pp. 210–220.

Liu X., Wang S., Lin J.C.-W., Liu S. An algorithm for overlapping chromosome segmentation based on region selection. Neural Comput. Appl. 2022:1–10. doi: 10.1007/s00521-022-07317-y. DOI

Bruno P., Calimeri F., Marte C., Manna M. Rules and Reasoning. Springer International Publishing; Cham, Switzerland: 2021. Combining deep learning and ASP-based models for the semantic segmentation of medical images; pp. 95–110.

Emara T., Munim H.E.A.E., Abbas H.M. LiteSeg: A novel lightweight ConvNet for semantic segmentation; Proceedings of the 2019 Digital Image Computing: Techniques and Applications (DICTA); Perth, Australia. 2–4 December 2019.

Qin Y., Kamnitsas K., Ancha S., Nanavati J., Cottrell G., Criminisi A., Nori A. Medical Image Computing and Computer Assisted Intervention—MICCAI 2018. Springer International Publishing; Cham, Switzerland: 2018. Autofocus Layer for Semantic Segmentation; pp. 603–611.

Fang F., Yao Y., Zhou T., Xie G., Lu J. Self-supervised multi-modal hybrid fusion network for brain tumor segmentation. IEEE J. Biomed. Health Inform. 2022;26:5310–5320. doi: 10.1109/JBHI.2021.3109301. PubMed DOI

Ding Y., Gong L., Zhang M., Li C., Qin Z. A multi-path adaptive fusion network for multimodal brain tumor segmentation. Neurocomputing. 2020;412:19–30. doi: 10.1016/j.neucom.2020.06.078. DOI

Jiang Y., Ye M., Wang P., Huang D., Lu X. MRF-IUNet: A multiresolution fusion brain tumor segmentation network based on improved inception U-Net. Comput. Math. Methods Med. 2022;2022:6305748. doi: 10.1155/2022/6305748. PubMed DOI PMC

Zhou T., Ruan S., Guo Y., Canu S. A multi-modality fusion network based on attention mechanism for brain tumor segmentation; Proceedings of the 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI); Iowa City, IA, USA. 3–7 April 2020.

Liu X., Xing F., El Fakhri G., Woo J. Self-semantic contour adaptation for cross modality brain tumor segmentation; Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI); Kolkata, India. 28–31 March 2022. PubMed PMC

Liu H., Liu M., Li D., Zheng W., Yin L., Wang R. Recent advances in pulse-coupled neural networks with applications in image processing. Electronics. 2022;11:3264. doi: 10.3390/electronics11203264. DOI

Kalinin A.A., Iglovikov V.I., Rakhlin A., Shvets A.A. Advances in Intelligent Systems and Computing. Springer Singapore; Singapore: 2020. Medical image segmentation using deep neural networks with pre-trained encoders; pp. 39–52.

Hatamizadeh A., Nath V., Tang Y., Yang D., Roth H.R., Xu D. Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. Springer International Publishing; Cham, Switzerland: 2022. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images; pp. 272–284.

Jha D., Riegler M.A., Johansen D., Halvorsen P., Johansen H.D. DoubleU-net: A deep convolutional neural network for medical image segmentation; Proceedings of the 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS); Rochester, MN, USA. 28–30 July 2020.

Nie D., Gao Y., Wang L., Shen D. Medical Image Computing and Computer Assisted Intervention—MICCAI 2018. Springer International Publishing; Cham, Switzerland: 2018. ASDNet: Attention based semi-supervised deep networks for medical image segmentation; pp. 370–378.

Ni J., Wu J., Tong J., Chen Z., Zhao J. GC-Net: Global context network for medical image segmentation. Comput. Methods Programs Biomed. 2020;190:105121. doi: 10.1016/j.cmpb.2019.105121. PubMed DOI

Wang L., Chen R., Wang S., Zeng N., Huang X., Liu C. Nested dilation network (NDN) for multi-task medical image segmentation. IEEE Access. 2019;7:44676–44685. doi: 10.1109/ACCESS.2019.2908386. DOI

Siddique N., Paheding S., Elkin C.P., Devabhaktuni V. U-net and its variants for medical image segmentation: A review of theory and applications. IEEE Access. 2021;9:82031–82057. doi: 10.1109/ACCESS.2021.3086020. DOI

GPU-Based Parallel Processing Techniques for Enhanced Brain Magnetic Resonance Imaging Analysis: A Review of Recent Advances