Purpose Fetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the blood flow in both fetuses. This procedure is particularly challenging due to the limited field of view, poor visibility, occasional bleeding, and poor image quality. Fetoscopic mosaicking can help in creating an image with the expanded field of view which could facilitate the clinicians during the TTTS procedure. Methods We propose a deep learning-based mosaicking framework for diverse fetoscopic videos captured from different settings such as simulation, phantoms, ex vivo, and in vivo environments. The proposed mosaicking framework extends an existing deep image homography model to handle video data by introducing the controlled data generation and consistent homography estimation modules. Training is performed on a small subset of fetoscopic images which are independent of the testing videos. Results We perform both quantitative and qualitative evaluations on 5 diverse fetoscopic videos (2400 frames) that captured different environments. To demonstrate the robustness of the proposed framework, a comparison is performed with the existing feature-based and deep image homography methods. Conclusion The proposed mosaicking framework outperformed existing methods and generated meaningful mosaic, while reducing the accumulated drift, even in the presence of visual challenges such as specular highlights, reflection, texture paucity, and low video resolution.

PurposeThe standard clinical treatment of Twin-to-Twin transfusion syndrome consists in the photo-coagulation of undesired anastomoses located on the placenta which are responsible to a blood transfer between the two twins. While being the standard of care procedure, fetoscopy suffers from a limited field-of-view of the placenta resulting in missed anastomoses. To facilitate the task of the clinician, building a global map of the placenta providing a larger overview of the vascular network is highly desired.MethodsTo overcome the challenging visual conditions inherent to in vivo sequences (low contrast, obstructions or presence of artifacts, among others), we propose the following contributions: (1) robust pairwise registration is achieved by aligning the orientation of the image gradients, and (2) difficulties regarding long-range consistency (e.g. due to the presence of outliers) is tackled via a bag-of-word strategy, which identifies overlapping frames of the sequence to be registered regardless of their respective location in time.ResultsIn addition to visual difficulties, in vivo sequences are characterised by the intrinsic absence of gold standard. We present mosaics motivating qualitatively our methodological choices and demonstrating their promising aspect. We also demonstrate semi-quantitatively, via visual inspection of registration results, the efficacy of our registration approach in comparison with two standard baselines.ConclusionThis paper proposes the first approach for the construction of mosaics of placenta in in vivo fetoscopy sequences. Robustness to visual challenges during registration and long-range temporal consistency are proposed, offering first positive results on in vivo data for which standard mosaicking techniques are not applicable.Electronic supplementary materialThe online version of this article (10.1007/s11548-018-1728-4) contains supplementary material, which is available to authorized users.

Abstract. The most effective treatment for twin-to-twin transfusion syndrome is laser photocoagulation of the shared vascular anastomoses in the placenta. Vascular connections are extremely challenging to locate due to their caliber and the reduced field-of-view of the fetoscope. Therefore, mosaicking techniques are beneficial to expand the scene, facilitate navigation, and allow vessel photocoagulation decision-making. Local vision-based mosaicking algorithms inherently drift over time due to the use of pairwise transformations. We propose the use of an electromagnetic tracker (EMT) sensor mounted at the tip of the fetoscope to obtain camera pose measurements, which we incorporate into a probabilistic framework with frame-to-frame visual information to achieve globally consistent sequential mosaics. We parametrize the problem in terms of plane and camera poses constrained by EMT measurements to enforce global consistency while leveraging pairwise image relationships in a sequential fashion through the use of local bundle adjustment. We show that our approach is drift-free and performs similarly to state-of-the-art global alignment techniques like bundle adjustment albeit with much less computational burden. Additionally, we propose a version of bundle adjustment that uses EMT information. We demonstrate the robustness to EMT noise and loss of visual information and evaluate mosaics for synthetic, phantom-based and ex vivo datasets.