Deep Generative Model Challenge for Domain Adaptation in Surgery 2021

Abstract

Mitral regurgitation (MR) is the second most frequent indication for valve surgery in Europe and may occur for organic or functional causes [1]. Mitral valve repair, although considerably more difficult, is prefered over mitral valve replacement, since the native tissue of the valve is preserved. It is a complex on-pump heart surgery, often conducted only by a handful of surgeons in high-volume centers. Minimally invasive procedures, which are performed with endoscopic video recordings, became more and more popular in recent years. However, data availability and data privacy concerns are still an issue for the development of automatic scene analysis algorithms. The AdaptOR challenge aims to address these issues by formulating a domain adaptation problem „from simulation to surgery“: We provide a smaller number of datasets from real surgeries, and a larger number of annotated recordings of training and planning sessions from a physical mitral valve simulator. The goal is to reduce the considerable domain gap between simulation and intraoperative cases, e.g. by incorporating generative models, as in [2,3]. The task associated to the domain adaptation itself is to detect a varying number of 2D landmarks per frame [4] in the target domain. The landmarks are defined by the placement of sutures during mitral annuloplasty (entry and exit points into the tissue), which renders useful for surgical skill assessment and detailed intraoperative documentation. The evaluation metrics of this challenge will be related to how well these points could be identified in unseen intraoperative scenes, therefore it is also possible to only come up with a solution to a landmark detection problem in a single domain. More complex methods, however, would leverage data from both domains and adapt them on input-, output-, and/or feature level. Due to the specific clinical motivation of improving the realism of surgical simulation [2,3], the AdaptOR challenge especially aims to provide a framework for comparison of the performance of different image-to-image translation approaches. Such approaches need to learn how to sucessfully transform the images into an intraoperative appearance, thereby not altering already realistic entities of the image (surgical instruments, sutures, needles etc.). While this can be merely assessed visually, and we will show example results during the workshop, we hypothesize that the success of landmark detection may be an indicator for the quality of the transfer with respect to the consistency of sutures in both domains. <strong>References</strong> [1] https://www.escardio.org/Journals/E-Journal-of-Cardiology-Practice/Volume-16/Mitral-valve-incompetence-epidemiology-and-causes [2] Engelhardt S., De Simone R., Full P.M., Karck M., Wolf I. (2018) Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries. In: Frangi A., Schnabel J., Davatzikos C., Alberola-López C., Fichtinger G. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. MICCAI 2018. Lecture Notes in Computer Science, vol 11070. Springer, Cham, doi: 10.1007/978-3-030-00928-1_84 [3] Engelhardt, S., Sharan, L., Karck, M., De Simone, R., Wolf, I. (2019), Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11768. Springer, Cham, pp 155-163, doi: https://doi.org/10.1007/978-3-030-32254-0_18

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