High-Precision Neuronal Segmentation: An Ensemble of YOLOX, Mask R-CNN, and UPerNet
DOI:
https://doi.org/10.53469/jtpes.2024.04(04).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.
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Copyright (c) 2024 Zhengning Li, Yibo Yin, Zibu Wei, Yang Luo, Guokun Xu, Ying Xie
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