Showing papers by "Ivo Wolf published in 2019"

Flexible and comprehensive patient-specific mitral valve silicone models with chordae tendineae made from 3D-printable molds

Abstract: Given the multitude of challenges surgeons face during mitral valve repair surgery, they should have a high confidence in handling of instruments and in the application of surgical techniques before they enter the operating room. Unfortunately, opportunities for surgical training of minimally invasive repair are very limited, leading to a situation where most surgeons undergo a steep learning curve while operating the first patients. In order to provide a realistic tool for surgical training, a commercial simulator was augmented by flexible patient-specific mitral valve replica. In an elaborated production pipeline, finalized after many optimization cycles, models were segmented from 3D ultrasound and then 3D-printable molds were computed automatically and printed in rigid material, the lower part being water-soluble. After silicone injection, the silicone model was dissolved from the mold and anchored in the simulator. To our knowledge, our models are the first to comprise the full mitral valve apparatus, i.e., the annulus, leaflets, chordae tendineae and papillary muscles. Nine different valve molds were automatically created according to the proposed workflow (seven prolapsed valves and two valves with functional mitral insufficiency). From these mold geometries, 16 replica were manufactured. A material test revealed that EcoflexTM 00-30 is the most suitable material for leaflet-mimicking tissue out of seven mixtures. Production time was around 36 h per valve. Twelve surgeons performed various surgical techniques, e.g., annuloplasty, neo-chordae implantation, triangular leaflet resection, and assessed the realism of the valves very positively. The standardized production process guarantees a high anatomical recapitulation of the silicone valves to the segmented models and the ultrasound data. Models are of unprecedented quality and maintain a high realism during haptic interaction with instruments and suture material.

Flexible and Comprehensive Patient-Specific Mitral Valve Silicone Models with Chordae Tendinae Made From 3D-Printable Molds.

Abstract: Given the multitude of challenges surgeons face during mitral valve repair surgery, they should have a high confidence in handling of instruments and in the application of surgical techniques before they enter the operating room. Unfortunately, opportunities for surgical training of minimally-invasive repair are very limited, leading to a situation where most surgeons undergo a steep learning curve while operating the first patients. In order to provide a realistic tool for surgical training, a commercial simulator was augmented by flexible patient-specific mitral valve replica. In an elaborated production pipeline, finalized after many optimization cycles, models were segmented from 3D ultrasound and then 3D-printable molds were computed automatically and printed in rigid material, the lower part being water-soluble. After silicone injection, the silicone model was dissolved from the mold and anchored in the simulator. To our knowledge, our models are the first to comprise the full mitral valve apparatus, i.e. the annulus, leaflets, chordae tendineae and papillary muscles. Nine different valve molds were automatically created according to the proposed workflow (seven prolapsed valves and two valves with functional mitral insufficiency). From these mold geometries, 16 replica were manufactured. A material test revealed that Ecoflex\textsuperscript{TM} 00-30 is the most suitable material for leaflet-mimicking tissue out of seven mixtures. Production time was around 36h per valve. Twelve surgeons performed various surgical techniques, e.g. annuloplasty, neo-chordae implantation, triangular leaflet resection and assessed the realism of the valves very positively. The standardized production process guarantees a high anatomical recapitulation of the silicone valves to the segmented models and the ultrasound data...

Replicated mitral valve models from real patients offer training opportunities for minimally invasive mitral valve repair.

Abstract: Objectives Minimally invasive mitral valve repair is considered a challenging procedure. Mastering the necessary skills takes years of training and clinical experience. To date, reconstructive surgery is performed mainly by a few surgeons with a strong track record, whereas trainees have only limited opportunities to practise. Methods A high-fidelity training simulator was equipped with novel silicone replicas of patient-specific mitral valves containing all of the anatomical components of the valve. The goal of this system was to aid members of the surgical community to overcome the steep learning curve. Results Twelve surgeons (5 experts and 7 surgical resident trainees) performed a minimally invasive mitral valve repair procedure on these models and assessed the usefulness for different applications. The trainees found the main application to be general surgical training and education for mitral valve repair, whereas the experts found the main benefit to be rehearsal for a specific patient. The skills of the trainees were improved in only a single session. The valve models placed in a water solution showed a high echogenicity. Conclusions Preoperative patient-specific simulation could improve the safety and effectiveness of mitral valve repair in the hands of a larger number of surgeons. Because the system is based on a quantitative segmentation of the anatomy of the mitral valve, it offers young surgeons training in general dexterity and also provides an exact numerical quantitative assessment of valvular geometry. This system can be used to educate surgeons to strive for and achieve well-defined and measurable surgical changes to the anatomy of the valve and to achieve the desired functional results.

Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training

Abstract: Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are based on deep image-to-image transformation methods. Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences. Conditioned on frames from the surgical training process, the learned models are able to generate impressive results by transforming unrealistic parts of the image (e.g. the uniform phantom texture is replaced by the more heterogeneous texture of the tissue). Image-to-image synthesis usually learns a mapping \(G:X~\rightarrow ~Y\) such that the distribution of images from G(X) is indistinguishable from the distribution Y. However, it does not necessarily force the generated images to be consistent and without artifacts. In the endoscopic image domain this can affect depth cues and stereo consistency of a stereo image pair, which ultimately impairs surgical vision. We propose a cross-domain conditional generative adversarial network approach (GAN) that aims to generate more consistent stereo pairs. The results show substantial improvements in depth perception and realism evaluated by 3 domain experts and 3 medical students on a 3D monitor over the baseline method. In 84 of 90 instances our proposed method was preferred or rated equal to the baseline.

Cross-Domain Conditional Generative Adversarial Networks for Stereoscopic Hyperrealism in Surgical Training

Abstract: Phantoms for surgical training are able to mimic cutting and suturing properties and patient-individual shape of organs, but lack a realistic visual appearance that captures the heterogeneity of surgical scenes. In order to overcome this in endoscopic approaches, hyperrealistic concepts have been proposed to be used in an augmented reality-setting, which are based on deep image-to-image transformation methods. Such concepts are able to generate realistic representations of phantoms learned from real intraoperative endoscopic sequences. Conditioned on frames from the surgical training process, the learned models are able to generate impressive results by transforming unrealistic parts of the image (e.g.\ the uniform phantom texture is replaced by the more heterogeneous texture of the tissue). Image-to-image synthesis usually learns a mapping $G:X~\to~Y$ such that the distribution of images from $G(X)$ is indistinguishable from the distribution $Y$. However, it does not necessarily force the generated images to be consistent and without artifacts. In the endoscopic image domain this can affect depth cues and stereo consistency of a stereo image pair, which ultimately impairs surgical vision. We propose a cross-domain conditional generative adversarial network approach (GAN) that aims to generate more consistent stereo pairs. The results show substantial improvements in depth perception and realism evaluated by 3 domain experts and 3 medical students on a 3D monitor over the baseline method. In 84 of 90 instances our proposed method was preferred or rated equal to the baseline.

Multimodal image registration of pre- and intra-interventional data for surgical planning of transarterial chemoembolisation

Abstract: Multimodal registration improves surgical planning and the performance of interventional procedures such as transarterial chemoembolizations (TACE), since it allows to combine complementary information provided by pre- and intrainterventional data about tumor localization and access. However, no registration methods specifically developed for the multimodal registration of abdominal scans exist and as a result only general-purpose methods are available for this application. In this paper, we evaluate and optimize the performance of three standard registration methods which rely on different similarity metrics, namely Advanced Mattes Mutual Information (AMMI), Advanced Normalized Correlation (ANC) and Normalized Mutual Information (NMI), for the registration of preinterventional T1- and T2-weighted MRI to preinterventional CT as well as intrainterventional Cone Beam CT (CBCT) to preinterventional CT of the liver. Moreover, different variants of the registration algorithms, based on the introduction of masks and different resolution levels in multistage registrations, are investigated. To evaluate the performance of each registration method, the capture range was estimated based on the calculation of the mean target registration error.

Improving Surgical Training Phantoms by Hyperrealism: Deep Unpaired Image-to-Image Translation from Real Surgeries.

Abstract: Current ‘dry lab’ surgical phantom simulators are a valuable tool for surgeons which allows them to improve their dexterity and skill with surgical instruments. These phantoms mimic the haptic and shape of organs of interest, but lack a realistic visual appearance. In this work, we present an innovative application in which representations learned from real intraoperative endoscopic sequences are transferred to a surgical phantom scenario. The term hyperrealism is introduced in this field, which we regard as a novel subform of surgical augmented reality for approaches that involve real-time object transfigurations. For related tasks in the computer vision community, unpaired cycle-consistent Generative Adversarial Networks (GANs) have shown excellent results on still RGB images. Though, application of this approach to continuous video frames can result in flickering, which turned out to be especially prominent for this application. Therefore, we propose an extension of cycle-consistent GANs, named tempCycleGAN, to improve temporal consistency. The novel method is evaluated on captures of a silicone phantom for training endoscopic reconstructive mitral valve procedures. Synthesized videos show highly realistic results with regard to (1) replacement of the silicone appearance of the phantom valve by intraoperative tissue texture, while (2) explicitly keeping crucial features in the scene, such as instruments, sutures and prostheses. Compared to the original CycleGAN approach, tempCycleGAN efficiently removes flickering between frames. The overall approach is expected to change the future design of surgical training simulators since the generated sequences clearly demonstrate the feasibility to enable a considerably more realistic training experience for minimally-invasive procedures.