Addressing the challenges of overlapping cell contours and morphological diversity in fluorescence cell image segmentation, this study proposes a segmentation algorithm that integrates an adaptive multi-scale attention mechanism with a boundary-sensitive loss function. Initially, to enhance the model's adaptability to the morphology of cells at various scales, an adaptive multi-scale channel attention mechanism was proposed, which, in conjunction with a feature pyramid, constructs a multi-scale attention pyramid structure, thereby improving the network's ability to extract features of complex cell shapes. Subsequently, a boundary-sensitive cross-entropy loss function was designed, which by assigning greater weights to the prediction of cell boundary regions, enhanced the network's precision in recognizing cell edges. Experimental outcomes indicate that the proposed method surpasses existing advanced models in terms of the average Dice coefficient (mDice) and the average Intersection over Union (mIoU) scores on fluorescence cell image datasets, substantiating the efficacy of the proposed method in the task of fluorescence image segmentation.