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Collagen is a key component of the extracellular matrix and well-oriented domains of collagen are relevant for mimicking the local cell environment in vitro. While there has been significant attention directed towards the alignment of collagen, formation of large-scale oriented domains remains a key challenge. Type I collagen self-assembles to form liquid crystalline (LC) mesophases in acidic conditions at concentrations above 100 mg/ml. The LC mesophase provides an efficient platform for large-scale alignment and patterning of collagen coated substrates. However, there exist challenges related to solubilizing and processing of collagen at such high concentrations in order to replicate the native extra cellular matrix (ECM). In this contribution, we report on centimeter-scale alignment in collagen-coated glass substrates using solutions that are well below the LC-forming concentrations. Importantly, we are also able to extend this method to create a mimic of the native ECM via macroscopic 3-D collagen hydrogels with programmed anisotropy within them. We explain the formation of these uniform domains via shear-induced and magnetically-induced liquid crystallinity of the collagen solutions. We show that the orientation, spreading and aspect ratio of Human Schwann Cells (HSCs) all are strongly coupled with the alignment of the collagen substrate/hydrogel. We use a simple Metroplis-based model to reveal that a critical magnetic field strength exists for a given concentration of collagen, exceeding which, macroscopic alignment is permissible- enabling guidance for future studies on alignment of collagen at high concentrations.

Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical translation. We propose to use marginal L1 average calibration error (mL1-ACE) as a novel auxiliary loss function to improve pixel-wise calibration without compromising segmentation quality. We show that this loss, despite using hard binning, is directly differentiable, bypassing the need for approximate but differentiable surrogate or soft binning approaches. Our work also introduces the concept of dataset reliability histograms which generalises standard reliability diagrams for refined visual assessment of calibration in semantic segmentation aggregated at the dataset level. Using mL1-ACE, we reduce average and maximum calibration error by 45% and 55% respectively, maintaining a Dice score of 87% on the BraTS 2021 dataset. We share our code here: https://github.com/cai4cai/ACE-DLIRIS

Surgical instrument segmentation is recognised as a key enabler to provide advanced surgical assistance and improve computer assisted interventions. In this work, we propose SegMatch, a semi supervised learning method to reduce the need for expensive annotation for laparoscopic and robotic surgical images. SegMatch builds on FixMatch, a widespread semi supervised classification pipeline combining consistency regularization and pseudo labelling, and adapts it for the purpose of segmentation. In our proposed SegMatch, the unlabelled images are weakly augmented and fed into the segmentation model to generate a pseudo-label to enforce the unsupervised loss against the output of the model for the adversarial augmented image on the pixels with a high confidence score. Our adaptation for segmentation tasks includes carefully considering the equivariance and invariance properties of the augmentation functions we rely on. To increase the relevance of our augmentations, we depart from using only handcrafted augmentations and introduce a trainable adversarial augmentation strategy. Our algorithm was evaluated on the MICCAI Instrument Segmentation Challenge datasets Robust-MIS 2019 and EndoVis 2017. Our results demonstrate that adding unlabelled data for training purposes allows us to surpass the performance of fully supervised approaches which are limited by the availability of training data in these challenges. SegMatch also outperforms a range of state-of-the-art semi-supervised learning semantic segmentation models in different labelled to unlabelled data ratios., Comment: preprint under review, 12 pages, 7 figures

Online surgical phase recognition plays a significant role towards building contextual tools that could quantify performance and oversee the execution of surgical workflows. Current approaches are limited since they train spatial feature extractors using frame-level supervision that could lead to incorrect predictions due to similar frames appearing at different phases, and poorly fuse local and global features due to computational constraints which can affect the analysis of long videos commonly encountered in surgical interventions. In this paper, we present a two-stage method, called Long Video Transformer (LoViT) for fusing short- and long-term temporal information that combines a temporally-rich spatial feature extractor and a multi-scale temporal aggregator consisting of two cascaded L-Trans modules based on self-attention, followed by a G-Informer module based on ProbSparse self-attention for processing global temporal information. The multi-scale temporal head then combines local and global features and classifies surgical phases using phase transition-aware supervision. Our approach outperforms state-of-the-art methods on the Cholec80 and AutoLaparo datasets consistently. Compared to Trans-SVNet, LoViT achieves a 2.4 pp (percentage point) improvement in video-level accuracy on Cholec80 and a 3.1 pp improvement on AutoLaparo. Moreover, it achieves a 5.3 pp improvement in phase-level Jaccard on AutoLaparo and a 1.55 pp improvement on Cholec80. Our results demonstrate the effectiveness of our approach in achieving state-of-the-art performance of surgical phase recognition on two datasets of different surgical procedures and temporal sequencing characteristics whilst introducing mechanisms that cope with long videos., Comment: Code link: https://github.com/MRUIL/LoViT

Visual discrimination of clinical tissue types remains challenging, with traditional RGB imaging providing limited contrast for such tasks. Hyperspectral imaging (HSI) is a promising technology providing rich spectral information that can extend far beyond three-channel RGB imaging. Moreover, recently developed snapshot HSI cameras enable real-time imaging with significant potential for clinical applications. Despite this, the investigation into the relative performance of HSI over RGB imaging for semantic segmentation purposes has been limited, particularly in the context of medical imaging. Here we compare the performance of state-of-the-art deep learning image segmentation methods when trained on hyperspectral images, RGB images, hyperspectral pixels (minus spatial context), and RGB pixels (disregarding spatial context). To achieve this, we employ the recently released Oral and Dental Spectral Image Database (ODSI-DB), which consists of 215 manually segmented dental reflectance spectral images with 35 different classes across 30 human subjects. The recent development of snapshot HSI cameras has made real-time clinical HSI a distinct possibility, though successful application requires a comprehensive understanding of the additional information HSI offers. Our work highlights the relative importance of spectral resolution, spectral range, and spatial information to both guide the development of HSI cameras and inform future clinical HSI applications.

The performance of deep learning (DL) algorithms is heavily influenced by the quantity and the quality of the annotated data. However, in Surgical Data Science, access to it is limited. It is thus unsurprising that substantial research efforts are made to develop methods aiming at mitigating the scarcity of annotated SDS data. In parallel, an increasing number of Computer Assisted Interventions (CAI) datasets are being released, although the scale of these remain limited. On these premises, data curation is becoming a key element of many SDS research endeavors. Surgical video datasets are demanding to curate and would benefit from dedicated support tools. In this work, we propose to summarize surgical videos into storyboards or collages of representative frames to ease visualization, annotation, and processing. Video summarization is well-established for natural images. However, state-of-the-art methods typically rely on models trained on human-made annotations, few methods have been evaluated on surgical videos, and the availability of software packages for the task is limited. We present videosum, an easy-to-use and open-source Python library to generate storyboards from surgical videos that contains a variety of unsupervised methods., Comment: Camera-ready version accepted at CRAS 2023

We report on our latest developments of a planar fiber-chip-coupling scheme, using angle polished, polarization maintaining (PM) fibers. Most integrated photonic chip components are polarization sensitive and a suitable way to launch several wavelength channels with the same polarization to the chip is the use of PM fibers. Those impose several challenges at processing and handling to achieve a stable, permanent, and low-loss coupling. We present the processing of the fibers in detail and experimental results for our planar and compact fiber-chip-coupling technique., Comment: 6 pages, 8 figures, conference paper for TWEPP2022, submitted to and reviewed by Journal of Instrumentation (JINST), to be typeset and published in JINST

Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several factors make reliable real-time estimation surprisingly challenging. The lack of rigorous investigation into the topic combined with the lack of a common benchmark dataset for this task has been a long-lasting issue in the field. In this paper, we propose two variants of a lean GPU-based computational pipeline combining edge detection and circle fitting. The two variants differ by relying on handcrafted features, and learned features respectively to extract content area edge point candidates. We also present a first-of-its-kind dataset of manually annotated and pseudo-labelled content areas across a range of surgical indications. To encourage further developments, the curated dataset, and an implementation of both algorithms, has been made public (https://doi.org/10.7303/syn32148000, https://github.com/charliebudd/torch-content-area). We compare our proposed algorithm with a state-of-the-art U-Net-based approach and demonstrate significant improvement in terms of both accuracy (Hausdorff distance: 6.3 px versus 118.1 px) and computational time (Average runtime per frame: 0.13 ms versus 11.2 ms)., Comment: Presented at AE-CAI MICCAI workshop

The paper investigates the problems of quickest change detection in Markov models and hidden Markov models (HMMs). Sequential observations are taken from a (hidden) Markov model. At some unknown time, an event occurs in the system and changes the transition kernel of the Markov model and/or the emission probability of the HMM. The objective is to detect the change quickly, while controlling the average running length (ARL) to false alarm. The data-driven setting is studied, where no knowledge of the pre-, post-change distributions is available. Kernel-based data-driven algorithms are developed, which can be applied in the setting with continuous state, can be updated in a recursive fashion, and are computationally efficient. Lower bounds on the ARL and upper bound on the worst-case average detection delay (WADD) are derived. The WADD is at most of the order of the logarithm of the ARL. The algorithms are further numerically validated on two practical problems of fault detection in DC microgrid and photovoltaic systems., Comment: 11 pages journal version

There is an ever increasing interest in the development of plasmonic 2D nanomaterials, with widespread applications in optoelectronics, high resolution microscopy, imaging and sensing, among others. With the current ability of ultrathin noble metal film deposition down to a few monolayers in thickness, there is a need for an analytical expression of the thickness dependent complex dielectric function for predicting optical properties for arbitrary thicknesses. The free and bound electron contributions to the dielectric function are dealt with independently, since their influences affect separate wavelengths ranges. The former is dealt within the Drude model framework for large wavelengths with appropriately addressed damping constant and plasma frequency parameters to account for thickness dependence. Applying our previously developed method, we determine these parameters for specific film thicknesses, based on refractive index experimental values for Ag and Au thin films. Fitting separately each one of these parameters allowed us to find an analytical expression for their dependence on arbitrary film thickness and consequently for the free electron contribution. Concerning bound electrons, it is seen that its contribution for small wavelengths is the same for all analyzed thicknesses and may be set equal to the bulk bound contribution. Taking all these facts into account, the complex dielectric function can be rewritten analytically, in terms of the bulk dielectric function plus corrective film thickness dependent terms. In particular, the fitting process for the damping constant allows us to determine that the electron scattering at the film boundary is mainly diffusive (inelastic) for both silver and gold thin films. It is also shown that, in accordance with theoretical studies, plasma frequency shows a red shift as the film thickness decreases.

The resolution of intelligence tests, in particular numerical sequences, has been of great interest in the evaluation of AI systems. We present a new computational model called KitBit that uses a reduced set of algorithms and their combinations to build a predictive model that finds the underlying pattern in numerical sequences, such as those included in IQ tests and others of much greater complexity. We present the fundamentals of the model and its application in different cases. First, the system is tested on a set of number series used in IQ tests collected from various sources. Next, our model is successfully applied on the sequences used to evaluate the models reported in the literature. In both cases, the system is capable of solving these types of problems in less than a second using standard computing power. Finally, KitBit's algorithms have been applied for the first time to the complete set of entire sequences of the well-known OEIS database. We find a pattern in the form of a list of algorithms and predict the following terms in the largest number of series to date. These results demonstrate the potential of KitBit to solve complex problems that could be represented numerically., Comment: 11 pages

A sensitivity conditioning control strategy is analyzed in this paper and it is applied to power electronic converters. The term is used to improve closed loop systems, such as power converters with cascaded inner and outer loop controllers. The impact of the sensitivity term is analyzed using singular perturbation theory. In addition, the implementation of the control term is addressed for practical systems, where the number of inputs is generally not sufficient for exact sensitivity conditioning. Simulation results are presented for a buck converter with output capacitor voltage regulation and a Permanent Magnet Synchronous Machine (PMSM), used as a generator with an active rectifier. Finally, experimental results are presented for the buck converter, demonstrating the advantages and feasibility in implementing the approximate sensitivity conditioning term for closed loop power converters.

The promise of active nanophotonics technology relies on the confinement and control of light at the nanoscale. Confinement via plasmonics, dielectric resonators, and waveguides can be complemented with materials whose optical properties can be controlled using nonlinear effects. Transparent conducting oxides (TCOs) exhibit strong optical nonlinearities in their near zero permittivity spectral region, on the femtosecond time-scale. Harnessing full control over the nonlinear response requires a deeper understanding of the process. To achieve this, we develop a self-consistent time-domain model for the nonlinear optical response of TCOs and implement it into a three-dimensional finite-difference time-domain code. We compare and tune our simulation tools against recently published experimental results for intense laser irradiation of thin indium tin oxide (ITO) films. Finally, by simulating intense laser irradiation of ITO-based plasmonic metasurfaces, we demonstrate the full power of our approach. As expected, we find validating the significant enhancement of the nonlinear response of an ITO-based metasurface over bare ITO thin films. Our work thus enables quantitative nanophotonics design with epsilon-near-zero materials.

Many keyhole interventions rely on bi-manual handling of surgical instruments, forcing the main surgeon to rely on a second surgeon to act as a camera assistant. In addition to the burden of excessively involving surgical staff, this may lead to reduced image stability, increased task completion time and sometimes errors due to the monotony of the task. Robotic endoscope holders, controlled by a set of basic instructions, have been proposed as an alternative, but their unnatural handling may increase the cognitive load of the (solo) surgeon, which hinders their clinical acceptance. More seamless integration in the surgical workflow would be achieved if robotic endoscope holders collaborated with the operating surgeon via semantically rich instructions that closely resemble instructions that would otherwise be issued to a human camera assistant, such as "focus on my right-hand instrument". As a proof of concept, this paper presents a novel system that paves the way towards a synergistic interaction between surgeons and robotic endoscope holders. The proposed platform allows the surgeon to perform a bimanual coordination and navigation task, while a robotic arm autonomously performs the endoscope positioning tasks. Within our system, we propose a novel tooltip localization method based on surgical tool segmentation and a novel visual servoing approach that ensures smooth and appropriate motion of the endoscope camera. We validate our vision pipeline and run a user study of this system. The clinical relevance of the study is ensured through the use of a laparoscopic exercise validated by the European Academy of Gynaecological Surgery which involves bi-manual coordination and navigation. Successful application of our proposed system provides a promising starting point towards broader clinical adoption of robotic endoscope holders., Comment: Caspar Gruijthuijsen and Luis C. Garcia-Peraza-Herrera have contributed equally to this work and share first authorship