  Mitral valve repair is a very difficult operation, often requiring
experienced surgeons. The doctor will insert a prosthetic ring to aid in the
restoration of heart function. The location of the prosthesis' sutures is
critical. Obtaining and studying them during the procedure is a valuable
learning experience for new surgeons. This paper proposes a landmark detection
network for detecting sutures in endoscopic pictures, which solves the problem
of a variable number of suture points in the images. Because there are two
datasets, one from the simulated domain and the other from real intraoperative
data, this work uses cycleGAN to interconvert the images from the two domains
to obtain a larger dataset and a better score on real intraoperative data. This
paper performed the tests using a simulated dataset of 2708 photos and a real
dataset of 2376 images. The mean sensitivity on the simulated dataset is about
75.64% and the precision is about 73.62%. The mean sensitivity on the real
dataset is about 50.23% and the precision is about 62.76%. The data is from the
AdaptOR MICCAI Challenge 2021, which can be found at
https://zenodo.org/record/4646979\#.YO1zLUxCQ2x.
