A Twin-to-Twin Transfusion Syndrome (TTTS) is a condition that occurs in about 10% of pregnancies involving monochorionic twins. This complication can be treated with fetoscopic laser coagulation. The procedure could greatly benefit from panorama reconstruction to gain an overview of the placenta. In previous work we investigated which steps could improve the reconstruction performance for an in-vivo setting. In this work we improved this registration by proposing a stable region detection method as well as extracting matchable features based on a deep-learning approach. Finally, we extracted a measure for the image registration quality and the visibility condition. With experiments we show that the image registration performance is increased and more constant. Using these methods a system can be developed that supports the surgeon during the surgery, by giving feedback and providing a more complete overview of the placenta.@en

Twin-to-Twin Transfusion Syndrome (TTTS) is a condition that occurs in about 10% of pregnancies involving monochorionic twins. This complication can be treated with fetoscopic laser coagulation. The procedure could greatly benefit from panorama reconstruction to gain an overview of the placenta. Current state-of-the-art methods focus on panorama reconstruction in an ex-vivo setting. However, these methods fail in the in-vivo surgical setting. This paper describes the panorama reconstruction approach, the challenges posed by the in-vivo setting and the influence of these challenges on the panorama reconstruction. With experiments we show that the viewing quality is greatly reduced and that the limited motion of the fetoscope complicates and limits the precision of the image registration. We also identify the aspect necessary to shift from ex-vivo to in-vivo panorama reconstruction. Following our recommendations it should be possible to develop an approach that can be applied to TTTS surgery@en