An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images

Muhammad Usman Saeed1, Bin Wang1, Jinfang Sheng1, Hussain Mobarak Albarakati2
1School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China
2Computer and Network Engineering Department, College of Computer and Information Systems, Umm Al-Qura University, Makkah, 24382, Saudi Arabia

Tóm tắt

Từ khóa


Tài liệu tham khảo

Glessgen, C.G., Cyriac, J., Yang, S., Manneck, S., Wichtmann, H.M., Wasserthal, J., Kovacs, B.K., Harder, D.: Segment and slice: A two-step deep learning pipeline for opportunistic vertebral fracture detection in computed tomography. In: medRxiv (2022). https://api.semanticscholar.org/CorpusID:254066460

Healthline: Spine. https://www.healthline.com/human-body-maps/sternum (2023)

Park, T., Yoon, M.A., Cho, Y.C., Ham, S.J., Ko, Y., Kim, S., Jeong, H., Lee, J.: Automated segmentation of the fractured vertebrae on ct and its applicability in a radiomics model to predict fracture malignancy. Scientific Reports 12 (2022)

Zhang, Q., Du, Y., Wei, Z., Liu, H., Yang, X., Zhao, D.: Spine medical image segmentation based on deep learning. Journal of Healthcare Engineering 2021 (2021)

Kim, K.C., Cho, H.C., Jang, T.J., Choi, J.M., Seo, J.K.: Automatic detection and segmentation of lumbar vertebrae from x-ray images for compression fracture evaluation. Computer Methods and Programs in Biomedicine 200, 105833 (2021) https://doi.org/10.1016/j.cmpb.2020.105833

Golla, A.-K., Lorenz, C., Buerger, C., Lossau, T., Klinder, T., Mutze, S., Arndt, H., Spohn, F., Mittmann, M., Goelz, L.: Cervical spine fracture detection in computed tomography using convolutional neural networks. Physics in Medicine & Biology 68(11), 115010 (2023) https://doi.org/10.1088/1361-6560/acd48b

Chan, Y.-K., Lin, C.-S., Lin, H.-J., Yip, K.-T.: Segmentation of spinal mri images and new compression fracture detection. Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (2022)

Benzakour, A., Altsitzioglou, P., Lemée, J.-M., Ahmad, A.A., Mavrogenis, A.F., Benzakour, T.: Artificial intelligence in spine surgery. International Orthopaedics 47, 457–465 (2022)

Small, J.E., Osler, P.M., Paul, A., Kunst, M.M.: Ct cervical spine fracture detection using a convolutional neural network. American Journal of Neuroradiology 42, 1341–1347 (2021)

Sunder, A., Chhabra, H.S., Aryal, A.: Geriatric spine fractures - demography, changing trends, challenges and special considerations: A narrative review. Journal of clinical orthopaedics and trauma 43, 102190 (2023)

Chen, X., Zhang, R., Yan, P.: Feature fusion encoder decoder network for automatic liver lesion segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 430–433 (2019)

Wu, H., Zhang, J., Huang, K.: Sparsemask: Differentiable connectivity learning for dense image prediction. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 6767–6776 (2019)

Zhang, Z., Zhang, X., Peng, C., Cheng, D., Sun, J.: Exfuse: Enhancing feature fusion for semantic segmentation. In: European Conference on Computer Vision (2018). https://api.semanticscholar.org/CorpusID:262349636

Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E.: Squeeze-and-excitation networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7132–7141 (2017)

Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K.P., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40, 834–848 (2016)

Bock, S., Weiß, M.G.: A proof of local convergence for the adam optimizer. 2019 International Joint Conference on Neural Networks (IJCNN), 1–8 (2019)

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. ArXiv abs/1505.04597 (2015)

Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, held in conjunction with MICCAI 2018, Granada, Spain, S... 11045, 3–11 (2018)

Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39, 2481–2495 (2015)

Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3431–3440 (2014)

Han, Z., Wei, B., Mercado, A., Leung, S., Li, S.: Spine-gan: Semantic segmentation of multiple spinal structures. Medical Image Analysis 50, 23–35 (2018)

Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: European Conference on Computer Vision (2018). https://api.semanticscholar.org/CorpusID:3638670