an easy and safe technique, liver transection by curettage and aspiration (LTCA), using an efficient instrument, Peng’s multifunction operative dissector (PMOD) is described in this article. A comparison brtween two groups of patients undergonig hapatectomy were made. One group were operated on with LTCA; the other group with forceps crush method (FCM). Results: the amount of blood loss with LTCA was only half of that with FCM. The time needed for liver transection with OTCA was 40 percent shorter than that with FCM. After comparision of a variety of currently available techniiques and surgical instruments, it is concluded that LTCA is superior to all other techniques, while PMOD is superior to CUSA.
For the endoscopic surgical instrument segmentation task, existing methods have failed to address the semantic gap caused by the mismatch between high-frequency spatial details and low-frequency semantic features in the U-shaped network (U-Net) architecture. This study proposes a U-Net algorithm based on frequency-domain adaptive feature decomposition and visual Mamba, namely frequency-domain decoupling Mamba U-Net (FDMUNet), for surgical instrument segmentation. This algorithm embeds a frequency-domain adaptive feature enhancement module into the skip connections, and decomposes features into high-frequency and low-frequency components through the Fourier transform and a learnable filter, followed by channel weighting and fusion, so as to enhance surgical instrument edge information and bridge the semantic gap between encoder and decoder features. On the Endoscopic Vision Challenge 2017 public dataset, FDMUNet achieved Intersection over Union, mean Intersection over Union, and mean class Intersection over Union scores of 70.79%, 74.25%, and 69.50%, respectively. In addition, the ablation experiment further verified the effectiveness of the proposed module. This method not only provides a new solution for instrument segmentation in complex scenes, but also provides a new research idea for the application of frequency-domain information in medical image segmentation.