Instance Segmentation of 2D Label-Free Microscopic Images using Deep Learning

B. A. Mohamed, Lamees N. Mahmoud, W. Al-Atabany, N. Salem
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Abstract

The precise detection and segmentation of cells in microscopic image sequences is an essential task in biomedical research, such as drug discovery and studying the development of tissues, organs, or entire organisms. However, the detection and segmentation of cells in phase contrast images with a halo and shade-off effects is still challenging. Lately, Mask Regional Convolutional Neural Network (Mask R-CNN) has been introduced for object detection and instance segmentation of natural images. This study investigates the efficacy of the Mask R-CNN to instantly detect and segment label-free microscopic images. The dataset used in this paper is taken from the ISBI cell tracking challenge. The Mask R-CNN is trained using different hyperparameters and compared to the U-Net model. Experimental results show that the Mask R-CNN model achieves 91.6 % when using ResNet-50 backbone and COCO weights.
基于深度学习的二维无标签显微图像实例分割
显微图像序列中细胞的精确检测和分割是生物医学研究中的一项重要任务,例如药物发现和研究组织、器官或整个生物体的发育。然而,具有晕晕和阴影效果的相衬图像中细胞的检测和分割仍然是一个挑战。近年来,Mask区域卷积神经网络(Mask R-CNN)被引入到自然图像的目标检测和实例分割中。本研究探讨了Mask R-CNN在即时检测和分割无标签显微图像方面的功效。本文使用的数据集来自ISBI单元跟踪挑战。Mask R-CNN使用不同的超参数进行训练,并与U-Net模型进行比较。实验结果表明,当使用ResNet-50主干网和COCO权值时,Mask R-CNN模型的识别率达到91.6%。
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