Deep neural networks (DNN) have been widely used to carry out segmentation tasks in both electron microscopy (EM) and light/fluorescence microscopy (LM/FM). Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show how Res-CR-Net, a new type of fully convolutional neural network that does not adopt a U-Net architecture, excels at segmentation tasks traditionally considered very hard, like recognizing the contours of nuclei, cytoplasm and mitochondria in densely packed cells in either EM or LM/FM images.

中文翻译：

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Res-CR-Net，这是一个具有新颖架构的残差网络，该架构针对显微镜图像的语义分割进行了优化

深度神经网络（DNN）已被广泛用于在电子显微镜（EM）和光/荧光显微镜（LM / FM）中执行分割任务。为此目的而开发的大多数DNN都是基于编解码器U-Net体系结构的某些变体。在这里，我们展示了Res-CR-Net（一种不采用U-Net架构的新型全卷积神经网络）如何擅长于传统上认为非常困难的分割任务，例如识别密集堆积的核，细胞质和线粒体的轮廓EM或LM / FM图像中的单元格。