Accurate breast nodule segmentation in ultrasound images is very challenging due to the low resolution and noise of ultrasound imaging， as well as the complexity and variability of the shape and texture of nodules， therefore， an end-to-end automatic segmentation method of breast nodules in ultrasound images based on multi-scale and cross-spatial feature fusion was proposed. Firstly， a Multi-scale Feature Extraction and Fusion （MFEF） module was designed to enable the network to have multi-scale feature extraction ability by fusing four convolutional paths with different receptive fields. Then， for the multi-scale observation and information filtering of high-level semantic information， a Scale-aware Feature Aggregation （SFA） module was used at the bottleneck layer to enhance the deep feature extraction ability in the encoding stage. Besides， a Cross-spatial Residual Fusion （CRF） module was designed and applied to the skip connection between the encoder and decoder to fuse information among different encoding layers in a cross-spatial way and implement information complementarity between different encoding layers， further extract information features of encoding layer and narrow the difference between peer layers of encoder and decoder to better compensate for the information loss in the decoding stage. Experimental results on a public ultrasound breast nodule dataset show that the proposed method achieves DICE coefficient of 0.888， which is 0.033 to 0.094 higher than those of the mainstream deep learning segmentation models UNet， AttUNet， ResUNet++ and SKUNet， and is 0.001 to 0.068 higher than those of the improved models such as CF²-Net， Estan， FS-UNet and SMU-Net in the same dataset. The subjective visualization of the segmentation result of the proposed method is closest to the gold standards provided by experts， verifying that the proposed mehtod can segment the breast nodule area more accurately.