MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences
MIMO U-Net: efficient cell segmentation and counting in microscopy image sequences
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DOI：
10.1117/12.2655627
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发表时间：
2023-04
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通讯作者：
P. Dave;Y. Kolinko;Hunter Morera;Kurtis Allen;Saeed S. Alahmari;Dmitry Goldgof;L. Hall;P. Mouton
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作者：
P. Dave;Y. Kolinko;Hunter Morera;Kurtis Allen;Saeed S. Alahmari;Dmitry Goldgof;L. Hall;P. Mouton
显微图像中的自动细胞定量可以加速生物医学研究。荧光显微镜下神经元的三维分割已经取得了重大进展。然而，由于低信噪比和来自散焦神经元的信号，它仍然是明场显微镜的一个挑战。亮场z堆栈中的自动神经元计数通常在扩展景深图像上或仅在一个厚焦平面图像上执行。然而，分辨位于不同z深度处的重叠单元是一个挑战。由于每个神经元在z轴上的分离，因此可以通过在其最佳焦点z平面中计数每个神经元来解决重叠问题。无偏体视学是细胞总数估计的最新技术。细胞的分割边界是必需的，以便将无偏计数规则用于体视学应用。因此，我们通过分段进行计数。我们建议通过将二进制分割任务作为多类多标签任务来实现最佳焦平面中的神经元分割。此外，我们建议在多输入多输出系统中有效地使用2D U-Net进行图像间特征学习，该系统将二进制分割任务作为多类多标签分割任务。我们证明了MIMO方法的准确性和效率，使用由专家本地准备的明场显微镜Z-堆栈数据集。所提出的MIMO方法也验证了从细胞跟踪挑战的数据集，实现可比的结果，比较方法配备了内存单元。我们的z-stack数据集可在https://tinyurl.com/wncfxn9m上找到。
Automatic cell quantification in microscopy images can accelerate biomedical research. There has been significant progress in the 3D segmentation of neurons in fluorescence microscopy. However, it remains a challenge in bright-field microscopy due to the low Signal-to-Noise Ratio and signals from out-of-focus neurons. Automatic neuron counting in bright-field z-stacks is often performed on Extended Depth of Field images or on only one thick focal plane image. However, resolving overlapping cells that are located at different z-depths is a challenge. The overlap can be resolved by counting every neuron in its best focus z-plane because of their separation on the z-axis. Unbiased stereology is the state-of-the-art for total cell number estimation. The segmentation boundary for cells is required in order to incorporate the unbiased counting rule for stereology application. Hence, we perform counting via segmentation. We propose to achieve neuron segmentation in the optimal focal plane by posing the binary segmentation task as a multi-class multi-label task. Also, we propose to efficiently use a 2D U-Net for inter-image feature learning in a Multiple Input Multiple Output system that poses a binary segmentation task as a multi-class multi-label segmentation task. We demonstrate the accuracy and efficiency of the MIMO approach using a bright-field microscopy z-stack dataset locally prepared by an expert. The proposed MIMO approach is also validated on a dataset from the Cell Tracking Challenge achieving comparable results to a compared method equipped with memory units. Our z-stack dataset is available at https://tinyurl.com/wncfxn9m.