The semantic segmentation of a brain tumor is the essential stage in medical treatment planning. Due to the different characteristics of tumors, one of the main difficulties in image segmentation is the severe imbalance between classes. Also, a dataset with imbalanced classes is a common problem in multimodal 3D brain MRIs. Despite these problems, most studies in brain tumor segmentation are biased toward the overrepresented tumor class (majority class) and ignore the small size tumor class (minority class). In this paper, we propose an improved loss function Weighted Focal Loss (WFL), based on 3D U-Net to enhance the prediction of brain tumor segmentation. Using our proposed loss function (WFL) solves the imbalance between classes and the imbalance between weights by giving higher weights to the minority and lower weights to the majority. After assigning these weights to different pixel values, our work is able to resolve pixel degradation, which is one of the limitations of the loss function during model training. Based on our experiments, the proposed function (WFL) on the 3D U-Net model for high-grade glioma (HGG) and low-grade glioma (LGG) in the Brain Tumor Segmentation Challenge (BraTS) 2019 dataset has shown promising results for tumor core (TC), whole tumor (WT) and enhanced tumor (ET) with average dice scores of HGG: 0.830, 0.913, 0.815 and Dice scores of LGG for TC: 0.731, WT: 0.775 and ET: 0.685. Moreover, we deployed our training on BraTS 2020 in which we obtained mean Dice scores of HGG: TC: 0.843, WT: 0.892, ET: 0.871 and Dice scores of LGG: 0.7501, 0.7985, 0.6103 for TC, WT and ET, respectively.
References
[1]
Goceri, E. (2020) CapsNet Topology to Classify Tumours from Brain Images and Comparative Evaluation. IET Image Processing, 14, 882-889. https://doi.org/10.1049/iet-ipr.2019.0312
[2]
Waite, K.A., Cioffi, G., Kruchko, C., Patil, N., Brat, D.J., Bruner, J.M. and Barnholtz-Sloan, J.S. (2022) Aligning the Central Brain Tumor Registry of the United States (CBTRUS) Histology Groupings with Current Definitions. Neuro-Oncology Practice. https://doi.org/10.1093/nop/npac025
[3]
Wang, J., Li, D., Yang, R., Tang, X., Yan, T. and Guo, W. (2020) Epidemiological Characteristics of 1385 Primary Sacral Tumors in One Institution in China. World Journal of Surgical Oncology, 18, 1-12. https://doi.org/10.1186/s12957-020-02045-w
[4]
Bacanin, N., Bezdan, T., Venkatachalam, K. and Al-Turjman, F. (2021) Optimized Convolutional Neural Network by Firefly Algorithm for Magnetic Resonance Image Classification of Glioma Brain Tumor Grade. Journal of Real-Time Image Processing, 18, 1085-1098. https://doi.org/10.1007/s11554-021-01106-x
[5]
Tan, L., Ma, W., Xia, J. and Sarker, S. (2021) Multimodal Magnetic Resonance Image Brain Tumor Segmentation Based on ACU-Net Network. IEEE Access, 9, 14608-14618. https://doi.org/10.1109/ACCESS.2021.3052514
[6]
Xiang, Z., Chen, X., Lv, Q. and Peng, X. (2021) A Novel Inflammatory lncRNAs Prognostic Signature for Predicting the Prognosis of Low-Grade Glioma Patients. Frontiers in Genetics, 12, Article ID: 697819. https://doi.org/10.3389/fgene.2021.697819
[7]
Jabbar, M., Hussain, F. and Dawood, S. (2022) Brain Tumor Augmentation Using the U-Net Architecture. EasyChair Preprint No. 7511.
[8]
Silva, M., Vivancos, C. and Duffau, H. (2022) The Concept of Peritumoral Zone in Diffuse Low-Grade Gliomas: Oncological and Functional Implications for a Connectome-Guided Therapeutic Attitude. Brain Sciences, 12, Article No. 504. https://doi.org/10.3390/brainsci12040504
[9]
Li, H., Hai, Z., Zou, L., Zhang, L., Wang, L., Wang, L. and Liang, G. (2022) Simultaneous Enhancement of T1 and T2 Magnetic Resonance Imaging of Liver Tumor at Respective Low and High Magnetic Fields. Theranostics, 12, 410-417. https://doi.org/10.7150/thno.67155
[10]
Wang, P., Weng, L., Xie, S., He, J., Ma, X., Bo, L.I., Gao, Y., et al. (2021) Primary Application of Mean Apparent Propagator-MRI Diffusion Model in the Grading of Diffuse Glioma. European Journal of Radiology, 138, Article ID: 109622. https://doi.org/10.1016/j.ejrad.2021.109622
[11]
Schad, L.R. (2022) Problems in Texture Analysis with Magnetic Resonance Imaging. Dialogues in Clinical Neuroscience, 6, 235-242.
[12]
Battista, J.J. (2022) Introduction to 3D Medical Imaging: Of Mice and Men, Music and Mummies. In: Van Dyk, J., Ed., True Tales of Medical Physics, Springer, Cham, 359-384. https://doi.org/10.1007/978-3-030-91724-1_16
[13]
Mamatha, S.K., Krishnappa, H.K. and Shalini, N. (2022) Graph Theory Based Segmentation of Magnetic Resonance Images for Brain Tumor Detection. Pattern Recognition and Image Analysis, 32, 153-161. https://doi.org/10.1134/S1054661821040167
[14]
Hankiewicz, J.H., Stoll, J.A., Stroud, J., Davidson, J., Livesey, K.L., Tvrdy, K., Celinski, Z.J., et al. (2019) Nano-Sized Ferrite Particles for Magnetic Resonance Imaging Thermometry. Journal of Magnetism and Magnetic Materials, 469, 550-557. https://doi.org/10.1016/j.jmmm.2018.09.037
[15]
Ali, M., Gilani, S.O., Waris, A., Zafar, K. and Jamil, M. (2020) Brain Tumour Image Segmentation Using Deep Networks. IEEE Access, 8, 153589-153598. https://doi.org/10.1109/ACCESS.2020.3018160
[16]
Wang, G., Li, W., Vercauteren, T. and Ourselin, S. (2019) Automatic Brain Tumour Segmentation Based on Cascaded Convolutional Neural Networks with Uncertainty Estimation. Frontiers in Computational Neuroscience, 13, Article No. 56. https://doi.org/10.3389/fncom.2019.00056
[17]
Ronneberger, O., Fischer, P. and Brox, T. (2015) U-Net: Convolutional Networks for Biomedical Image Segmentation. In: Navab, N., Hornegger, J., Wells, W.M. and Frangi, A.F., Eds., International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 234-241. https://doi.org/10.1007/978-3-319-24574-4_28
[18]
Çiçek, Ö., Abdulkadir, A., Lienkamp, S.S., Brox, T. and Ronneberger, O. (2016) 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. In: Ourselin, S., et al., Eds., International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 424-432. https://doi.org/10.1007/978-3-319-46723-8_49
[19]
Ma, C. and Li, X. (2021) Multi-Modal Brain Tumor Image Segmentation Based on Improved U-Net Model. 2021 IEEE 5th Information Technology, Networking, Electronic and Automation Control Conference, Vol. 5, 706-710. https://doi.org/10.1109/ITNEC52019.2021.9587180
[20]
AboElenein, N.M., Piao, S., Noor, A. and Ahmed, P.N. (2022) MIRAU-Net: An Improved Neural Network Based on U-Net for Gliomas Segmentation. Signal Processing: Image Communication, 101, Article ID: 116553. https://doi.org/10.1016/j.image.2021.116553
[21]
Wang, F., Jiang, R., Zheng, L., Meng, C. and Biswal, B. (2019) 3d u-Net Based Brain Tumor Segmentation and Survival Days Prediction. In: Crimi, A. and Bakas, S., Eds., International MICCAI Brainlesion Workshop, Springer, Cham, 131-141. https://doi.org/10.1007/978-3-030-46640-4_13
[22]
Wang, W., Chen, C., Ding, M., Yu, H., Zha, S. and Li, J. (2021) Transbts: Multimodal Brain Tumor Segmentation Using Transformer. In: de Bruijne, M., et al., Eds., International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, 109-119. https://doi.org/10.1007/978-3-030-87193-2_11
[23]
Sheng, N., Liu, D., Zhang, J., Che, C. and Zhang, J. (2021) Second-Order ResU-Net for Automatic MRI Brain Tumor Segmentation. Mathematical Biosciences and Engineering, 18, 4943-4960. https://doi.org/10.3934/mbe.2021251
[24]
Raza, R., Bajwa, U.I., Mehmood, Y., Anwar, M.W. and Jamal, M.H. (2022) dResU-Net: 3D Deep Residual U-Net Based Brain Tumor Segmentation from Multimodal MRI. Biomedical Signal Processing and Control, 79, Article ID: 103861. https://doi.org/10.2139/ssrn.4024177
[25]
Parmar, B. and Parikh, M. (2020) Brain Tumor Segmentation and Survival Prediction Using Patch Based Modified 3D U-Net. In: Crimi, A. and Bakas, S., Eds., Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, Cham, 398-409. https://doi.org/10.1007/978-3-030-72087-2_35
[26]
Chato, L., Kachroo, P. and Latifi, S. (2020) An Automatic Overall Survival Time Prediction System for Glioma Brain Tumor Patients Based on Volumetric and Shape Features. In: Crimi, A. and Bakas, S., Eds., Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, Springer, Cham, 352-365. https://doi.org/10.1007/978-3-030-72087-2_31
Tustison, N.J., Avants, B.B., Cook, P.A., Zheng, Y., Egan, A., Yushkevich, P.A. and Gee, J.C. (2010) N4ITK: Improved N3 Bias Correction. IEEE Transactions on Medical Imaging, 29, 1310-1320. https://doi.org/10.1109/TMI.2010.2046908
[30]
Lin, T.Y., Goyal, P., Girshick, R., He, K. and Dollár, P. (2017) Focal Loss for Dense Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, 22-29 October 2017, 2980-2988. https://doi.org/10.1109/ICCV.2017.324
[31]
Zhao, R., Qian, B., Zhang, X., Li, Y., Wei, R., Liu, Y. and Pan, Y. (2020) Rethinking Dice Loss for Medical Image Segmentation. 2020 IEEE International Conference on Data Mining (ICDM), Sorrento, 17-20 November 2020, 851-860. https://doi.org/10.1109/ICDM50108.2020.00094
[32]
Akil, M., Saouli, R. and Kachouri, R. (2020) Fully Automatic Brain Tumor Segmentation with Deep Learning-Based Selective Attention Using Overlapping Patches and Multi-Class Weighted Cross-Entropy. Medical Image Analysis, 63, Article ID: 101692. https://doi.org/10.1016/j.media.2020.101692
[33]
Zhou, X., Li, X., Hu, K., Zhang, Y., Chen, Z. and Gao, X. (2021) ERV-Net: An Efficient 3D Residual Neural Network for Brain Tumor Segmentation. Expert Systems with Applications, 170, Article ID: 114566. https://doi.org/10.1016/j.eswa.2021.114566
[34]
Latif, U., Shahid, A.R., Raza, B., Ziauddin, S. and Khan, M.A. (2021) An End-to-End Brain Tumor Segmentation System Using Multi-Inception-UNET. International Journal of Imaging Systems and Technology, 31, 1803-1816. https://doi.org/10.1002/ima.22585
[35]
Wong, K.C., Moradi, M., Tang, H. and Syeda-Mahmood, T. (2018) 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes. In: Frangi, A.F., et al., Eds., Medical Image Computing and Computer Assisted Intervention—MICCAI 2018, Springer, Cham, 612-619. https://doi.org/10.1007/978-3-030-00931-1_70
