  Mitral valve repair is a surgery to restore the function of the mitral valve.
To achieve this, a prosthetic ring is sewed onto the mitral annulus. Analyzing
the sutures, which are punctured through the annulus for ring implantation, can
be useful in surgical skill assessment, for quantitative surgery and for
positioning a virtual prosthetic ring model in the scene via augmented reality.
This work presents a neural network approach which detects the sutures in
endoscopic images of mitral valve repair and therefore solves a landmark
detection problem with varying amount of landmarks, as opposed to most other
existing deep learning-based landmark detection approaches. The neural network
is trained separately on two data collections from different domains with the
same architecture and hyperparameter settings. The datasets consist of more
than 1,300 stereo frame pairs each, with a total over 60,000 annotated
landmarks. The proposed heatmap-based neural network achieves a mean positive
predictive value (PPV) of 66.68$\pm$4.67% and a mean true positive rate (TPR)
of 24.45$\pm$5.06% on the intraoperative test dataset and a mean PPV of
81.50\pm5.77\% and a mean TPR of 61.60$\pm$6.11% on a dataset recorded during
surgical simulation. The best detection results are achieved when the camera is
positioned above the mitral valve with good illumination. A detection from a
sideward view is also possible if the mitral valve is well perceptible.
