Abstract：Objective To explore the automatic segmentation algorithm for tissue image segmentation in medical ultrasonography.
Methods This paper proposed an an end-to-end co-segmentation model, which consisted of encoder-decoder, similarity learning
module and attention learning module. The features of a pair of input ultrasound images were extracted by an encoder, optimized
by a similarity learning module, enhanced by an attention module. Finally, the decoder output the automatic prediction of the tissue
prospect. The proposed method was validated on a multi-category ultrasound image dataset containing three types of medical
ultrasound images (fetus, thyroid and breast). Results For pixel accuracy, accuracy and Jaccard similarity coefficient, the pixel
accuracy of 97.25%, accuracy of 94.51% and Jaccard similarity coefficient of 0.90 were obtained, respectively. Further, the accuracy
and effectiveness of the proposed method were verified by comparison and ablation experiments. Conclusion The proposed cosegmentation
model has high segmentation accuracy for multiple types of medical ultrasound images, and can quickly and accurately
obtain tissue regions, which is helpful for computer-aided diagnosis.
叶浩然，陈芳，谢彦廷，万鹏. 基于相似注意力的医学超声影像协同分割[J]. 中国医疗设备, 2023, 38(3): 14-20.
YE Haoran, CHEN Fang, XIE Yanting, WAN Peng. Medical Ultrasound Image Co-Segmentation Based on Similar Attention. China Medical Devices, 2023, 38(3): 14-20.
