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The traditional methods for cell counting are labor\u2010intensive, time\u2010consuming, and prone to human errors. Recently, deep learning\u2010based cell counting methods have become a trend, including point\u2010based counting methods, such as cell detection and cell density prediction, and non\u2010point\u2010based counting, such as cell number regression prediction. However, the point\u2010based counting method heavily relies on well\u2010annotated datasets, which are scarce and difficult to obtain. On the other hand, nonpoint\u2010based counting is less interpretable. The task of cell counting by dividing it into two subtasks is approached: cell number prediction and cell distribution prediction. To accomplish this, a deep learning network for spatial\u2010based super\u2010resolution reconstruction (SSRNet) is proposed that predicts the cell count and segments the cell distribution contour. To effectively train the model, an optimized multitask loss function (OM loss) is proposed that coordinates the training of multiple tasks. In SSRNet, a spatial\u2010based super\u2010resolution fast upsampling module (SSR\u2010upsampling) is proposed for feature map enhancement and one\u2010step upsampling, which can enlarge the deep feature map by 32 times without blurring and achieves fine\u2010grained detail and fast processing. SSRNet uses an optimized encoder network. Compared with the classic U\u2010Net, SSRNet's running memory read and write consumption is only 1\/10 of that of U\u2010Net, and the total number of multiply and add calculations is 1\/20 of that of U\u2010Net. Compared with the traditional sampling method, SSR\u2010upsampling can complete the upsampling of the entire decoder stage at one time, reducing the complexity of the network and achieving better performance. Experiments demonstrate that the method achieves state\u2010of\u2010the\u2010art performance in cell counting and segmentation tasks. The method achieves nonpoint\u2010based counting, eliminating the need for exact position annotation of each cell in the image during training. As a result, it has demonstrated excellent performance on cell counting and segmentation tasks. 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