High-Precision Neuronal Segmentation: An Ensemble of YOLOX, Mask R-CNN, and UPerNet
DOI:
https://doi.org/10.53469/jtpes.2024.04(05).08Keywords:
Cell Segmentation, YOLOX, Mask R-CNN, UPerNet, Deep Learning, Microscopic Image AnalysisAbstract
In the realm of neuron cell segmentation from microscopic images, computer vision technologies have shown potential in accelerating drug discovery processes for neurological disorders. This paper presents an innovative approach that combines YOLOX for object detection, Mask R-CNN for instance segmentation, and UPerNet for semantic segmentation to precisely delineate individual cells. Our methodology emphasizes advanced data preprocessing techniques and model ensembling to improve detection and segmentation of neuronal cell types. Extensive experiments conducted on the Sartorius Cell Instance Segmentation dataset demonstrate the superiority of our approach, achieving state-of-the-art results.
References
Wu, H., Souedet, N., Jan, C., Clouchoux, C., & Delzescaux, T. (2022). A general deep learning framework for neuron instance segmentation based on efficient UNet and morphological post-processing. Computers in Biology and Medicine, 150, 106180.
Kar, A., Petit, M., Refahi, Y., Cerutti, G., Godin, C., & Traas, J. (2022). Benchmarking of deep learning algorithms for 3D instance segmentation of confocal image datasets. PLoS computational biology, 18(4), e1009879.
Vadori, V., Peruffo, A., Graïc, J. M., Finos, L., Corain, L., & Grisan, E. (2023, October). NCIS: Deep Color Gradient Maps Regression and Three-Class Pixel Classification for Enhanced Neuronal Cell Instance Segmentation in Nissl-Stained Histological Images. In International Workshop on Machine Learning in Medical Imaging (pp. 457-466). Cham: Springer Nature Switzerland.
Peng, Q., Zheng, C., & Chen, C. (2023). Source-free domain adaptive human pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4826-4836).
Kirschbaum, E., Bailoni, A., & Hamprecht, F. A. (2020). DISCo for the CIA: deep learning, instance segmentation, and correlations for calcium imaging analysis. Preprint at https://arxiv. org/abs/1908.07957 v4.
Vadori, V., Graïc, J. M., Finos, L., Corain, L., Peruffo, A., & Grisan, E. (2023, April). MR-NOM: multi-scale resolution of neuronal cells in Nissl-stained histological slices via deliberate over-segmentation and merging. In 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) (pp. 1-5). IEEE.
Yang, L., Ghosh, R. P., Franklin, J. M., Chen, S., You, C., Narayan, R. R., ... & Liphardt, J. T. (2020). NuSeT: A deep learning tool for reliably separating and analyzing crowded cells. PLoS computational biology, 16(9), e1008193.
Hu, T., Xu, X., Chen, S., & Liu, Q. (2021). Accurate neuronal soma segmentation using 3D multi-task learning U-shaped fully convolutional neural networks. Frontiers in Neuroanatomy, 14, 592806.
Peng, Q., Zheng, C., & Chen, C. (2024). A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation. arXiv preprint arXiv:2403.11310.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI blog, 1(8), 9.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.
Popokh, L., Su, J., Nair, S., & Olinick, E. (2021, September). IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement. In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-7). IEEE.
Liu, T., Xu, C., Qiao, Y., Jiang, C., & Chen, W. (2024). News recommendation with attention mechanism. arXiv preprint arXiv:2402.07422.
Bao, W., Che, H., & Zhang, J. (2020, December). Will_Go at SemEval-2020 Task 3: An accurate model for predicting the (graded) effect of context in word similarity based on BERT. In Proceedings of the Fourteenth Workshop on Semantic Evaluation (pp. 301-306).
Zhou, Z., Xu, C., Qiao, Y., Ni, F., & Xiong, J. (2024). An Analysis of the Application of Machine Learning in Network Security. Journal of Industrial Engineering and Applied Science, 2(2), 5-12.
Su, J., Nair, S., & Popokh, L. (2023, February). EdgeGYM: a reinforcement learning environment for constraint-aware NFV resource allocation. In 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
Luo, Y., Wei, Z., Xu, G., Li, Z., Xie, Y., & Yin, Y. (2024). Enhancing E-commerce Chatbots with Falcon-7B and 16-bit Full Quantization. Journal of Theory and Practice of Engineering Science, 4(02), 52-57.
Xu, C., Yu, J., Chen, W., & Xiong, J. (2024, January). Deep learning in photovoltaic power generation forecasting: Cnn-lstm hybrid neural network exploration and research. In The 3rd International scientific and practical conference “Technologies in education in schools and universities”(January 23-26, 2024) Athens, Greece. International Science Group. 2024. 363 p. (p. 295).
Liu, T., Cai, Q., Xu, C., Zhou, Z., Xiong, J., Qiao, Y., & Yang, T. (2024). Image Captioning in news report scenario. arXiv preprint arXiv:2403.16209.
Xiong, J., Feng, M., Wang, X., Jiang, C., Zhang, N., & Zhao, Z. (2024). Decoding sentiments: Enhancing covid-19 tweet analysis through bert-rcnn fusion. Journal of Theory and Practice of Engineering Science, 4(01), 86-93.
Su, J., Nair, S., & Popokh, L. (2022, November). Optimal resource allocation in sdn/nfv-enabled networks via deep reinforcement learning. In 2022 IEEE Ninth International Conference on Communications and Networking (ComNet) (pp. 1-7). IEEE.
Xie, Y., Li, Z., Yin, Y., Wei, Z., Xu, G., & Luo, Y. (2024). Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification. Journal of Theory and Practice of Engineering Science, 4(02), 15-22.
Liu, T., Xu, C., Qiao, Y., Jiang, C., & Yu, J. (2024). Particle Filter SLAM for Vehicle Localization. arXiv preprint arXiv:2402.07429.
Wang, X., Qiao, Y., Xiong, J., Zhao, Z., Zhang, N., Feng, M., & Jiang, C. (2024). Advanced Network Intrusion Detection with TabTransformer. Journal of Theory and Practice of Engineering Science, 4(03), 191-198.
Qiao, Y., Ni, F., Xia, T., Chen, W., & Xiong, J. (2024, January). Automatic recognition of static phenomena in retouched images: A novel approach. In The 1st International scientific and practical conference “Advanced technologies for the implementation of new ideas”(January 09-12, 2024) Brussels, Belgium. International Science Group. 2024. 349 p. (p. 287).
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024). Accelerating Semi-Asynchronous Federated Learning. arXiv preprint arXiv:2402.10991.
Liu, S., Wu, K., Jiang, C., Huang, B., & Ma, D. (2023). Financial time-series forecasting: Towards synergizing performance and interpretability within a hybrid machine learning approach. arXiv preprint arXiv:2401.00534.
Zhao, Z., Zhang, N., Xiong, J., Feng, M., Jiang, C., & Wang, X. (2024). Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN. Journal of Theory and Practice of Engineering Science, 4(02), 38-44.
Liu, T., Cai, Q., Xu, C., Zhou, Z., Xiong, J., Qiao, Y., & Yang, T. (2024). Image Captioning in news report scenario. arXiv preprint arXiv:2403.16209.
Zhou, Z., Xu, C., Qiao, Y., Xiong, J., & Yu, J. (2024). Enhancing Equipment Health Prediction with Enhanced SMOTE-KNN. Journal of Industrial Engineering and Applied Science, 2(2), 13-20.
Yin, Y., Xu, G., Xie, Y., Luo, Y., Wei, Z., & Li, Z. (2024). Utilizing Deep Learning for Crystal System Classification in Lithium-Ion Batteries. Journal of Theory and Practice of Engineering Science, 4(03), 199-206.
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Copyright (c) 2024 Zhengning Li, Yibo Yin, Zibu Wei, Yang Luo, Guokun Xu, Ying Xie
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