Surgical Video Motion Magnification with Suppression of Instrument Artefacts

Video motion magnification can make blood vessels in surgical video more apparent by exaggerating their pulsatile motion and could prevent inadvertent damage and bleeding due to their increased prominence. It could also indicate the success of restricting blood supply to an organ when using a vessel clamp. However, the direct application to surgical video could result in aberration artefacts caused by its sensitivity to residual motion from the surgical instruments and would impede its practical usage in the operating theatre. By storing the previously obtained jerk filter response of each spatial component of each image frame - both prior to surgical instrument introduction and adhering to a Eulerian frame of reference - it is possible to prevent such aberrations from occurring. The comparison of the current readings to the prior readings of a single cardiac cycle at the corresponding cycle point, are used to determine if motion magnification should be active for each spatial component of the surgical video at that given point in time. In this paper, we demonstrate this technique and incorporate a scaling variable to loosen the effect which accounts for variabilities and misalignments in the temporal domain. We present promising results on endoscopic transnasal transsphenoidal pituitary surgery with a quantitative comparison to recent methods using Structural Similarity (SSIM), as well as qualitative analysis by comparing spatio-temporal cross sections of the videos and individual frames

HAPNet: hierarchically aggregated pyramid network for real-time stereo matching

©Recovering the 3D shape of the surgical site is crucial for multiple computer-assisted interventions. Stereo endoscopes can be used to compute 3D depth but computational stereo is a challenging, non-convex and inherently discontinuous optimisation problem. In this paper, we propose a deep learning architecture which avoids the explicit construction of a cost volume of similarity which is one of the most computationally costly blocks of stereo algorithms. This makes training our network significantly more efficient and avoids the needs for large memory allocation. Our method performs well, especially around regions comprising multiple discontinuities around surgical instrumentation or around complex small structures and instruments. The method compares well to the state-of-the-art techniques while taking a different methodological angle to computational stereo problem in surgical video

Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy

PURPOSE: Colorectal cancer is the third most common cancer worldwide, and early therapeutic treatment of precancerous tissue during colonoscopy is crucial for better prognosis and can be curative. Navigation within the colon and comprehensive inspection of the endoluminal tissue are key to successful colonoscopy but can vary with the skill and experience of the endoscopist. Computer-assisted interventions in colonoscopy can provide better support tools for mapping the colon to ensure complete examination and for automatically detecting abnormal tissue regions. METHODS: We train the conditional generative adversarial network pix2pix, to transform monocular endoscopic images to depth, which can be a building block in a navigational pipeline or be used to measure the size of polyps during colonoscopy. To overcome the lack of labelled training data in endoscopy, we propose to use simulation environments and to additionally train the generator and discriminator of the model on unlabelled real video frames in order to adapt to real colonoscopy environments. RESULTS: We report promising results on synthetic, phantom and real datasets and show that generative models outperform discriminative models when predicting depth from colonoscopy images, in terms of both accuracy and robustness towards changes in domains. CONCLUSIONS: Training the discriminator and generator of the model on real images, we show that our model performs implicit domain adaptation, which is a key step towards bridging the gap between synthetic and real data. Importantly, we demonstrate the feasibility of training a single model to predict depth from both synthetic and real images without the need for explicit, unsupervised transformer networks mapping between the domains of synthetic and real data

Integrated multi-modality image-guided navigation for neurosurgery: open-source software platform using state-of-the-art clinical hardware.

PURPOSE: Image-guided surgery (IGS) is an integral part of modern neuro-oncology surgery. Navigated ultrasound provides the surgeon with reconstructed views of ultrasound data, but no commercial system presently permits its integration with other essential non-imaging-based intraoperative monitoring modalities such as intraoperative neuromonitoring. Such a system would be particularly useful in skull base neurosurgery. METHODS: We established functional and technical requirements of an integrated multi-modality IGS system tailored for skull base surgery with the ability to incorporate: (1) preoperative MRI data and associated 3D volume reconstructions, (2) real-time intraoperative neurophysiological data and (3) live reconstructed 3D ultrasound. We created an open-source software platform to integrate with readily available commercial hardware. We tested the accuracy of the system's ultrasound navigation and reconstruction using a polyvinyl alcohol phantom model and simulated the use of the complete navigation system in a clinical operating room using a patient-specific phantom model. RESULTS: Experimental validation of the system's navigated ultrasound component demonstrated accuracy of [Formula: see text] and a frame rate of 25 frames per second. Clinical simulation confirmed that system assembly was straightforward, could be achieved in a clinically acceptable time of [Formula: see text] and performed with a clinically acceptable level of accuracy. CONCLUSION: We present an integrated open-source research platform for multi-modality IGS. The present prototype system was tailored for neurosurgery and met all minimum design requirements focused on skull base surgery. Future work aims to optimise the system further by addressing the remaining target requirements

Surgical Video Motion Magnification with Suppression of Instrument Artefacts

Video motion magnification could directly highlight subsurface blood vessels in endoscopic video in order to prevent inadvertent damage and bleeding. Applying motion filters to the full surgical image is however sensitive to residual motion from the surgical instruments and can impede practical application due to aberration motion artefacts. By storing the temporal filter response from local spatial frequency information for a single cardiovascular cycle prior to tool introduction to the scene, a filter can be used to determine if motion magnification should be active for a spatial region of the surgical image. In this paper, we propose a strategy to reduce aberration due to non-physiological motion for surgical video motion magnification. We present promising results on endoscopic transnasal transsphenoidal pituitary surgery with a quantitative comparison to recent methods using Structural Similarity (SSIM), as well as qualitative analysis by comparing spatio-temporal cross sections of the videos and individual frames.Comment: Early accept to the Internation Conference on Medical Imaging Computing and Computer Assisted Intervention (MICCAI) 2020 Presentation available here: https://www.youtube.com/watch?v=kKI_Ygny76Q Supplementary video available here: https://www.youtube.com/watch?v=8DUkcHI149

Examining in vivo tympanic membrane mobility using smart phone video-otoscopy and phase-based eulerian video magnification

The tympanic membrane (TM) is the bridging element between the pressure waves of sound in air and the ossicular chain. It allows for sound to be conducted into the inner ear, achieving the human sense of hearing. Otitis media with effusion (OME, commonly referred to as ‘glue ear’) is a typical condition in infants that prevents the vibration of the TM and causes conductive hearing loss, this can lead to stunting early stage development if undiagnosed. Furthermore, OME is hard to identify in this age group; as they cannot respond to typical audiometry tests. Tympanometry allows for the mobility of the TM to be examined without patient response, but requires expensive apparatus and specialist training. By combining a smartphone equipped with a 240 frames per second video recording capability with an otoscopic clip-on accessory, this paper presents a novel application of Eulerian Video Magnification (EVM) to video-otology, that could provide assistance in diagnosing OME. We present preliminary results showing a spatio-temporal slice taken from an exaggerated video visualization of the TM being excited in vivo on a healthy ear. Our preliminary results demonstrate the potential for using such an approach for diagnosing OME under visual inspection as alternative to tympanometry, which could be used remotely and hence help diagnosis in a wider population pool