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Digital light microscopy techniques are among the most widely used methods in cell biology and medical research. Despite that, the automated classification of objects such as cells or specific parts of tissues in images is difficult. We present an approach to classify confluent cell layers in microscopy images by learned deep correlation features using deep neural networks. These deep correlation features are generated through the use of gram-based correlation features and are input to a neural network for learning the correlation between them. In this work we wanted to prove if a representation of cell data based on this is suitable for its classification as has been done for artworks with respect to their artistic period. The method generates images that contain recognizable characteristics of a specific cell type, for example, the average size and the ordered pattern.
Ultra wideband real-time locating system for tracking people and devices in the operating room
(2022)
Position tracking within the OR could be one possible input for intraoperative situation recognition. Our approach demonstrates a Real-time Locating System (RTLS) using the Ultra Wideband (UWB) technology to determine the position of people or objects. The UWB RTLS was integrated into the research OR at Reutlingen University and the system’s settings were optimized regarding the four factors accuracy, susceptibility to interference, range, and latency. Therefore, different parameters were adapted and the effects on the factors were compared. Goodtracking quality could be achieved under optimal settings. These results indicate that a UWB RTLS is well suited to determine the position of people and devices in our setting. The feasibility of the system needsto be evaluated under real OR conditions.
With the progress of technology in modern hospitals, an intelligent perioperative situation recognition will gain more relevance due to its potential to substantially improve surgical workflows by providing situation knowledge in real-time. Such knowledge can be extracted from image data by machine learning techniques but poses a privacy threat to the staff’s and patients’ personal data. De-identification is a possible solution for removing visual sensitive information. In this work, we developed a YOLO v3 based prototype to detect sensitive areas in the image in real-time. These are then deidentified using common image obfuscation techniques. Our approach shows that it is principle suitable for de-identifying sensitive data in OR images and contributes to a privacyrespectful way of processing in the context of situation recognition in the OR.
Towards Automated Surgical Documentation using automatically generated checklists from BPMN models
(2021)
The documentation of surgeries is usually created from memory only after the operation, which is an additional effort for the surgeon and afflicted with the possibility of imprecisely, shortend reports. The display of process steps in the form of checklists and the automatic creation of surgical documentation from the completed process steps could serve as a reminder, standardize the surgical procedure and save time for the surgeon. Based on two works from Reutlingen University, which implemented the creation of dynamic checklists from Business Process Modelling Notation (BPMN) models and the storage of times at which a process step was completed, a prototype was developed for an android tablet, to expand the dynamic checklists by functions such as uploading photos and files, manual user entries, the interception of foreseeable deviations from the normal course of operations and the automatic creation of OR documentation.
Intraoperative brain deformation, so called brain shift, affects the applicability of preoperative magnetic resonance imaging (MRI) data to assist the procedures of intraoperative ultrasound (iUS) guidance during neurosurgery. This paper proposes a deep learning-based approach for fast and accurate deformable registration of preoperative MRI to iUS images to correct brain shift. Based on the architecture of 3D convolutional neural networks, the proposed deep MRI-iUS registration method has been successfully tested and evaluated on the retrospective evaluation of cerebral tumors (RESECT) dataset. This study showed that our proposed method outperforms other registration methods in previous studies with an average mean squared error (MSE) of 85. Moreover, this method can register three 3D MRI-US pair in less than a second, improving the expected outcomes of brain surgery.
In the current study the in vitro outcome of a degradable magnesium alloy (AZ91D) and standard titanium modified by nanostructured-hydroxyapatite (n-HA) coatings concerning cell adhesion and osteogenic differentiation was investigated by direct cell culture. The n-HA modification was prepared via radio-frequency magnetron sputtering deposition and proven by field emission scanning electron microscopy and X-ray powder diffraction patterns revealing a homogenous surface coating. Human mesenchymal stem cell (hMSCs) adhesion was examined after one and 14 days displaying an enhanced initial cell adhesion on the n-HA modified samples. The osteogenic lineage commitment of the cells was determined by alkaline phosphatase (ALP) quantification. On day one n-HA coated AZ91D exhibited a comparable ALP expression to standard tissue culture polystyrene samples. However, after 14 days solely little DNA and ALP amounts were measurable on n-HA coated AZ91D due to the lack of adherent cells. Titanium displayed excellent cell adhesion properties and ALP was detectable after 14 days. An increased pH of the culture was measured for AZ91D as well as for n-HA coated AZ91D. We conclude that n-HA modification improves initial cell attachment on AZ91D within the first 24 h. However, the effect does not ersist for 14 days in in vitro conditions.
In vitro, hydrogel-based ECMs for functionalizing surfaces of various material have played an essential role in mimicking native tissue matrix. Polydimethylsiloxane (PDMS) is widely used to build microfluidic or organ-on-chip devices compatible with cells due to its easy handling in cast replication. Despite such advantages, the limitation of PDMS is its hydrophobic surface property. To improve wettability of PDMS-based devices, alginate, a naturally derived polysaccharide, was covalently bound to the PDMS surface. This alginate then crosslinked further hydrogel onto the PDMS surface in desired layer thickness. Hydrogel-modified PDMS was used for coating a topography chip system and in vitro investigation of cell growth on the surfaces. Moreover, such hydrophilic hydrogel-coated PDMS is utilized in a microfluidic device to prevent unspecific absorption of organic solutions. Hence, in both exemplary studies, PDMS surface properties were modified leading to improved devices.
Thermoplastic polycarbonate urethane elastomers (TPCU) are potential implant materials for treating degenerative joint diseases thanks to their adjustable rubber-like properties, their toughness, and their durability. We developed a water-containing high-molecular-weight sulfated hyaluronic acid-coating to improve the interaction of TPCU with the synovial fluid. It is suggested that trapped synovial fluid can act as a lubricant that reduces the friction forces and thus provides an enhanced abrasion resistance of TPCU implants. Aims of this work were (i) the development of a coating method for novel soft TPCU with high-molecular sulfated hyaluronic acid to increase the biocompatibility and (ii) the in vitro validation of the functionalized TPCUs in cell culture experiments.
Purpose
Computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research.
Methods
Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/ output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods.
Results
The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform’s performance considerably outperforms other 3D Slicer cloud-based approaches.
Conclusions
Developed Slicer-DeepSeg allows the development of novel AI-assisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at github.com/razeineldin/Slicer-DeepSeg.
Vitamin E (VitE) additives are important in treating osteoarthritis inclusive cartilage regeneration due to their antioxidant and anti-inflammatory properties. The present research study focuses on the ability of biological antioxidant VitE (alpha-tocopherol isoform) to reduce or minimize oxidative degradation of soft implantable polyurethane (PU) elastomers after extended periods of time (5 months) in vitro. The effect of the oxidation storage media on the morphology of the segmented PUs was evaluated by mechanical softening, crystallization and melting behavior of both soft and hard segments (SS, HS) using dynamic mechanical analysis (DMA). Bulk mechanical properties of the potential implant materials during ageing were predicted from comprehensive mechanical testing of the biomaterials under tension and compression cyclic loads. 5-months in vitro data suggest that the prepared siloxane-poly(carbonate urethane) formulations have sufficient resistance against degradation to be suitable materials for chondral long term bio-stable implants. Most importantly, the positive effect of incorporating VitE (0.5 or 1.0% w/w) as bio-antioxidant and lubricant on the bio-stability was observed for all PU types. VitE-additives protected the surface layer from erosion and cracking during chemical oxidation in vitro as well as from thermal oxidation during extrusion re-processing.
Polyelectrolyte multi-layer (PEM) coatings are prepared by alternative deposition of single polyelectrolyte monolayers on charged surfaces using the Layer-by-Layer (LbL) dip coating procedure. These are nanometre scaled coatings which allow fulfilling of different technical or biological requirements. The build-up process is based on selfassembly and self organization of polycations and polyanions on different substrates including complex geometrical structures and even closed volumes, forming homogeneous layer without defects. Depending on the proper selection of the applied polyelectrolytes, coatings with different stabilities can be prepared. Some of the coatings are stable and cannot be removed from the surface. Others are degradable and can be used as systems for controlled local drug delivery. Here we summarise the results of our experience in preparation of PEM coatings with different functionalities. PEM coatings can be used as controllable delivery system for siRNA polyplexes. They can be used to control the adhesion of different cell types on the surfaces and support e.g. the endothelialisation process on cardio-vascular medical devices as e.g. stents or reduce the immunological response of the tissue after implantation. We summarise results from physical characterisation of the coatings (e.g. film thickness, roughness, electrical charge and hydrophilicity) combined with in-vitro biological studies on adhesion of HUVEC cells.
Socially interactive robots with human-like speech synthesis and recognition, coupled with humanoid appearance, are an important subject of robotics and artificial intelligence research. Modern solutions have matured enough to provide simple services to human users. To make the interaction with them as fast and intuitive as possible, researchers strive to create transparent interfaces close to human-human interaction. Because facial expressions play a central role in human-human communication, robot faces were implemented with varying degrees of human-likeness and expressiveness. We propose a way to implement a program that believably animates changing facial expressions and allows to influence them via inter-process communication based on an emotion model. This will can be used to create a screen based virtual face for a robotic system with an inviting appearance to stimulate users to seek interaction with the robot.
Optofluidics
(2019)
This introduction into the multidisciplinary area of optofluidics offers the necessary foundations in photonics, polymer physics and process analytics to students, engineers and researchers to enter the field. All basic ingredients of a polymer-based platform as a foundation for quick and compact solutions for chemical, biological and medical sensing and manipulation are developed.
Adapting characteristics of biomaterials specifically for in vitro and in vivo applications is becoming increasingly important in order to control interactions between material and biological systems. These complex interactions are influenced by surface properties like chemical composition, charge, mechanical and topographic attributes. In many cases it is not useful or even not possible to alter the base material but changing surface, to improve biocompatibility or to make surfaces bioactive, may be achieved by thin coatings. An already established method is the coating with polyelectrolyte multilayers (PEM). To adjust adhesion, proliferation and improve vitality of certain cell types, we modified the roughness of PEM coatings. We included different types nanoparticles (NP’s) in different concentrations into PEM coatings for controlling surface roughness. Surface properties were characterized and the reaction of 3 different cell types on these coatings was tested.
Soft lithography, a tool widely applied in biology and life sciences with numerous applications, uses the soft molding of photolithography-generated master structures by polymers. The central part of a photolithography set-up is a mask-aligner mostly based on a high-pressure mercury lamp as an ultraviolet (UV) light source. This type of light source requires a high level of maintenance and shows a decreasing intensity over its lifetime, influencing the lithography outcome. In this paper, we present a low-cost, bench-top photolithography tool based on ninety-eight 375 nm light-emitting diodes (LEDs). With approx. 10 W, our presented lithography set-up requires only a fraction of the energy of a conventional lamp, the LEDs have a guaranteed lifetime of 1000 h, which becomes noticeable by at least 2.5 to 15 times more exposure cycles compared to a standard light source and with costs less than 850 C it is very affordable. Such a set-up is not only attractive to small academic and industrial fabrication facilities who want to enable work with the technology of photolithography and cannot afford a conventional set-up, but also microfluidic teaching laboratories and microfluidic research and development laboratories, in general, could benefit from this cost-effective alternative. With our self-built photolithography system, we were able to produce structures from 6 μm to 50 μm in height and 10 μm to 200 μm in width. As an optional feature, we present a scaled-down laminar flow hood to enable a dust-free working environment for the photolithography process.
The paper describes how eye-tracking can be used to explore electronic patient records (EPR) in a sterile environment. As an information display, we used a system that we developed for the presentation of patient data and for supporting surgical hand disinfection. The eye-tracking was performed using the Tobii Eye Tracker 4C, and the connection between the eye-tracker and the HTML website was realized using the Tobii EyeX Chrome Extension. Interactions with the EPR are triggered by fixations of icons. The interaction was working as intended, but test persons reported a high mental load while using the system.
Access to clinical information during interventions is an important aspect to support the surgeon and his team in the OR. The OR-Pad research project aims at displaying clinically relevant information close to the patient during surgery. With the OR-Pad system, the surgeon shall be able to access case-specific information, displayed on a sterile-packaged, portable display device. Therefore, information shall be prepared before surgery and also be available afterwards. The project follows an user-centered design process. Within the third iteration, the interaction concept was finalized, resulting in an application that can be used in two modes, mobile and intraoperative, to support the surgeon before/after and during surgery, respectively. By supporting the surgeon perioperatively, it is expected to improve the information situation in the OR and thereby the quality of surgical results. Based on this concept, the system architecture was designed in detail, using a client-server architecture. Components, communication interfaces, exchanged data, and intended standards for data exchange of the OR-Pad system including connecting systems were conceived. Expert interviews by using a clickable prototype were conducted to evaluate the concepts.
A series of novel biomedical TPCUs with different percentages of hard segment and a silicone component in the soft segment were synthesized in a multi stage one-pot method. The kinetic profiles of the urethane formation in TPCU-based copolymer systems were monitored by rheological, in line FTIR spectroscopic (React IR) and real-time calorimetric (RC1) methods. This process-analytically monitored multi step synthesis was successfully used to optimize the production of medical-grade TPCU elastomers on preparative scale (in lots of several kg) with controlled molecular structure and mechanical properties. Various surface and bulk analytical methods as well as systematic studies of the mechanic response of the elastomer end-products towards compression and tensile loading were used to estimate the bio-stability of the prepared TPCUs in vitro after 3 months. The tests suggested that high bio-stability of all polyurethane formulations using accelerating in vitro test can be attributed to the synthetic design as well as to the specific techniques used for specimen preparation, namely: (1) the annealing for reducing residual polymer surface stress and preventing IES, (2) stabilization of the morphology by long time storage of the specimens after processing before being immersed in the test liquids, (3) purification by extraction to remove the shot chain oligomers which are the most susceptible to degradation. All mechanical tests were performed on cylindrical and circular disc specimens for modelling the thickness of the meniscus implants under application-relevant stress conditions.
Analysis of multicellular patterns is required to understand tissue organizational processes. By using a multi-scale object oriented image processing method, the spatial information of cells can be extracted automatically. Instead of manual segmentation or indirect measurements, such as general distribution of contrast or flow, the orientation and distribution of individual cells is extracted for quantitative analysis. Relevant objects are identified by feature queries and no low-level knowledge of image processing is required.
In bioprinting approaches, the choice of bioink plays an important role since it must be processable with the selected printing method, but also cytocompatible and biofunctional. Therefore, a crosslinkable gelatin-based ink was modified with hydroxyapatite (HAp) particles, representing the composite buildup of natural bone. The inks’ viscosity was significantly increased by the addition of HAp, making the material processable with extrusion-based methods. The storage moduli of the formed hydrogels rose significantly, depicting improved mechanical properties. A cytocompatibility assay revealed suitable ranges for photoinitiator and HAp concentrations. As a proof of concept, the modified ink was printed together with cells, yielding stable three-dimensional constructs containing a homogeneously distributed mineralization and viable cells.
In vivo, cells encounter different physical and chemical signals in the extracellular matrix (ECM) which regulate their behavior. Examples of these signals are micro- and nanometer-sized features, the rigidity, and the chemical composition of the ECM. The study of cell responses to such cues is important to understand complex cell functions, some diseases, and is basis for the development of new biomaterials for applications in medical implants or regenerative medicine. Therefore, the development of new methods for surface modifications with controlled physical and chemical features is crucial. In this work, we report a new combination of micelle nanolithography (BCML) and soft micro-lithography, for the production of polyethylene glycol (PEG) hydrogels, with a micro-grooved surface and decoration with hexagonally precisely arranged gold nanoparticles (AU NPs). The Au-NPs are used for binding adhesive ligands in a well-defined density. First tests were performed by culturing human fibroblasts on the gels. Adhesion and alignment of the cells along the parallel grooves of the surface were investigated. The substrates could provide a new platform for studying cell contact guidance by micro structures, and may enable a more precise control of cell behavior by nanometrically controlled surface functionalization.
Natural extracellular matrix (ECM) represents an ideal biomaterial for tissue engineering and regenerative medicine approaches. For further functionalization, there is a need for specific addressable functional groups within this biomaterial. Metabolic glycoengineering (MGE) provides a technique to incorporate modified monosaccharide derivatives into the ECM during their assembly, which was shown by us earlier for the production of a modified fibroblast-derived dermal ECM.
Focal adhesion clusters (FAC) are dynamic and complex structures that help cells to sense physicochemical properties of their environment. Research in biomaterials, cell adhesion or cell migration often involves the visualization of FAC by fluorescence staining and microscopy, which necessitates quantitative analysis of FAC and other cell features in microscopy images using image processing. Fluorescence microscopy images of human umbilical vein endothelial cells (HUVEC) obtained at 63x magnification were quantitatively analysed using ImageJ software. A generalised algorithm for selective segmentation and morphological analysis of FAC, nucleus and cell morphology is implemented. Further, a method for discrimination of FACnear the nucleus and around the periphery is implemented using masks. Our algorithm is able to effectively quantify different morphological characteristics of cell components and shows a high sensitivity and specificity while providing a modular software implementation.
Knee osteoarthritis is a common complication and can lead to total loss of joint function in patients. Treatment by either partial or total knee replacement with appropriate UHMWPE based implantsis highly invasive, may cause complications and may show unsatisfying results. Alternatively, treatment may be done by insertion of an elastic interpositional knee spacer with optimized material characteristics.
We report the development of high performance polyurethane-based polymers modified with bioactive molecules for fabrication of such knee spacers. In order to tailor mechanical and tribological properties and to improve resist to enzymatic degradation we propose a core-shell model for the spacer with specifically adapted properties.
Hearing contact lens (HCL) is a new type of hearing aid devices. One of its main components is a piezo-electric actuator (PEA). In order to evaluate and maximizethe HCL´s performance, a model of the HCL coupled to the middle ear was developed using finite element (FE)approach. To validate the model, vibrational measurements on the HCL and temporal bones were performed using a Laser-Doppler-Vibrometer (LDV). The model was validated step by step starting with HCL only. Then a silicone cap was fitted onto the HCL to provide an interface between the HCL and the tympanic membrane. The HCL was placed on the tympanic membrane and additional measurements were performed to validate the coupled model. The model was used to evaluate the sensitivity of geometrical and material parameters with respect to performance measures of the HCL. Moreover, deeper insight was gained into the feedback behavior, which causes whistling sounds, and the contact between the HCL and tympanic membrane.
Scanning Near-Field Optical Microscopy (SNOM) has developed during recent decades into a valuable tool to optically image the surface topology of materials with super-resolution. With aperture-based SNOM systems, the resolution scales with the size of the aperture, but also limits the sensitivity of the detection and thus the application for spectroscopic techniques like Raman SNOM. In this paper we report the extension of solid immersion lens (SIL) technology to Raman SNOM. The hemispherical SIL with a tip on the bottom acts as an apertureless dielectric nanoprobe for simultaneously acquiring topographic and spectroscopic information. The SIL is placed between the sample and the microscope objective of a confocal Raman microscope. The lateral resolution in the Raman mode is validated with a cross section of a semiconductor layer system and, at approximately 180 nm, is beyond the classical diffraction limit of Abbe.
Medical implants play a central role in modern medicine and both, naturally derived and synthetic materials have been explored as biomaterials for such devices. However, when implanted into living tissue, most materials initiate a host response. In addition, implants often cause bacterial infections leading to complications. Polyelectrolyte multilayer (PEM) coatings can be used for functionalization of medical implants improving the implant integration and reducing foreign body reactions. Some PEMs are also known to show antibacterial properties. We developed a PEM coating suggesting that it can decrease the risk of bacterial infections occurring after implantation while being highly biocompatible. We applied two different standard tests for evaluating the PEM’s antibacterial properties, the ISO norm (ISO 22196) and one ASTM norm (ASTM E2180) test. We found a reduction of bacterial growth on the PEM but to a different degree depending on the testing method. This result demonstrates the need for defining proper method to evaluate antibacterial properties of surface coatings.
Polyurethane-bases block copolymers (TPCUs) are block-copolymers with systematically varied soft and hard segments. They have been suggested to serve as material for chondral implants in joint regeneration. Such applications may require the adhesion of chondrocytes to the implant surface, facilitating cell growth while keeping their phenotype. Thus, aims of this work were (1) to modify the surface of soft biostable polyurethane-based model implants (TPCU and TSiPCU) with high-molecular weight hyaluronic acid (HA) using an optimized multistep strategy of immobilization, and (2) to evaluate bioactivity of the modified TPCUs in vitro. Our results show no cytotoxic potential of the TPCUs. HAbioactive molecules (Mw =700kDa) were immobilized onto the polyurethane surface via polyethylenimine (PEI) spacers, and modifications were confirmed by several characterization methods. Tests with porcine chondrocytes indicated the potential of the TPCU-HA for inducing enhanced cell proliferation.
Cell-cell and cell-extracellular matrix (ECM) adhesion regulates fundamental cellular functions and is crucial for cell-material contact. Adhesion is influenced by many factors like affinity and specificity of the receptor-ligand interaction or overall ligand concentration and density. To investigate molecular details of cell ECM and cadherins (cell-cell) interaction in vascular cells functional nanostructured surfaces were used Ligand-functionalized gold nanoparticles (AuNPs) with 6-8 nm diameter, are precisely immobilized on a surface and separated by non-adhesive regions so that individual integrins or cadherins can specifically interact with the ligands on the AuNPs. Using 40 nm and 90 nm distances between the AuNPs and functionalized either with peptide motifs of the extracellular matrix (RGD or REDV) or vascular endothelial cadherins (VEC), the influence of distance and ligand specificity on spreading and adhesion of endothelial cells (ECs) and smooth muscle cells (SMCs) was investigated. We demonstrate that RGD-dependent adhesion of vascular cells is similar to other cell types and that the distance dependence for integrin binding to ECM-peptides is also valid for the REDV motif. VEC-ligands decrease adhesion significantly on the tested ligand distances. These results may be helpful for future improvements in vascular tissue engineering and for development of implant surfaces.
Recent advances in artificial intelligence have enabled promising applications in neurosurgery that can enhance patient outcomes and minimize risks. This paper presents a novel system that utilizes AI to aid neurosurgeons in precisely identifying and localizing brain tumors. The system was trained on a dataset of brain MRI scans and utilized deep learning algorithms for segmentation and classification. Evaluation of the system on a separate set of brain MRI scans demonstrated an average Dice similarity coefficient of 0.87. The system was also evaluated through a user experience test involving the Department of Neurosurgery at the University Hospital Ulm, with results showing significant improvements in accuracy, efficiency, and reduced cognitive load and stress levels. Additionally, the system has demonstrated adaptability to various surgical scenarios and provides personalized guidance to users. These findings indicate the potential for AI to enhance the quality of neurosurgical interventions and improve patient outcomes. Future work will explore integrating this system with robotic surgical tools for minimally invasive surgeries.
Endogenous electrical fields play an important role in various physiological and pathological events. Yet the effects of electrical cues on processes such as wound healing, tumor development or metastasis are still rarely investigated, though it is known that direct current electrical fields can alter cell migration or proliferation in vitro. Several 2D experimental models for studying cell responses to direct current electrical fields have been presented and characterized but suitable experimental models for electrotaxis studies in 3D are rare. Here we present a novel, easy-to-produce, multi-well-based galvanotactic-chamber for the use in 2D and 3D cell experiments for investigations on the influence of electrical fields on tumor cell migration and tumor spheroid growth. Our presented system allows the simultaneous application of electrical field to cells in four chambers, either cultured on the bottom of the culture-plate (2D) or embedded in hydrogel filled channels(3D). The set-up is also suitable for, live-cell-imaging. Validation tests show stable electrical fields and high cell viabilities inside the channel. Tumor spheroids of various diameters can be exposed to direct current electrical fields up to one week.
Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train The CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labelled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.
We present an approach for segmenting individual cells and lamellipodia in epithelial cell clusters using fully convolutional neural networks. The method will set the basis for measuring cell cluster dynamics and expansion to improve the investigation of collective cell migration phenomena. The fully learning-based front-end avoids classical feature engineering, yet the network architecture needs to be designed carefully. Our network predicts how likely each pixel belongs to one of the classes and, thus, is able to segment the image. Besides characterizing segmentation performance, we discuss how the network will be further employed.
Intraoperative imaging can assist neurosurgeons to define brain tumours and other surrounding brain structures. Interventional ultrasound (iUS) is a convenient modality with fast scan times. However, iUS data may suffer from noise and artefacts which limit their interpretation during brain surgery. In this work, we use two deep learning networks, namely UNet and TransUNet, to make automatic and accurate segmentation of the brain tumour in iUS data. Experiments were conducted on a dataset of 27 iUS volumes. The outcomes show that using a transformer with UNet is advantageous providing an efficient segmentation modelling long-range dependencies between each iUS image. In particular, the enhanced TransUNet was able to predict cavity segmentation in iUS data with an inference rate of more than 125 FPS. These promising results suggest that deep learning networks can be successfully deployed to assist neurosurgeons in the operating room.
Surface topographies are often discussed as an important parameter influencing basic cell behavior. Whereas most in vitro studies deal with microstructures with sharp edges, smooth, curved microscale topographies might be more relevant concerning in-vivo situations. Addressing the lack of highly defined surfaces with varying curvature, we present a topography chip system with 3D curved features of varying spacing, curvature radii as well as varying overall dimensions of curved surfaces. The CurvChip is produced by low-cost photolithography with thermal reflow, subsequent (repetitive) PDMS molding and hot embossing. The platform facilitates the systematic in-vitro investigation of the impact of substrate curvature on cell types like epithelial, endothelial, smooth muscle cells, or stem cells. Such investigations will not only help to further understand the mechanism of curvature sensation but may also contribute to optimize cell-material interactions in the field of regenerative medicine.
Polyelectrolyte multilayer coatings (PEM) are prepared by alternative layer-by-layer deposition of cationic and anionic polyelectrolyte monolayers on charged surfaces. The thickness of the coatings ranges from nm to few μm. Their properties such as roughness, stiffness, surface charge and surface energy can be precisely tuned to fulfil different technical or biological requirements. The coating process is based on self-assembly of polyelectrolytes. Advantages of these coatings are their easy handling, no harsh chemistry and the possibility for coatings on complex geometries. The PEM coatings can be prepared from a variety of suitable polyelectrolytes. Their stability varies from very durable PEM coatings that are only soluble in strong solvents to quickly degradable, which may be applied as drug release system. One example of such a degradable PEM system is the one based on the polyelectrolyte pair Hyaluronan (HA) and Chitosan (CHI). These biopolymers originate from natural sources and show low toxicity towards human cells. However, HA/CHI multilayers show only weak adhesiveness for human umbilical vein endothelial cells (HUVEC). In this article, we summarize our approaches to enhance the HA/CHI multilayer by incorporation of a non-polymer substance –graphene oxide– to improve the cell adhesion and keep such properties as low cytotoxicity and biodegradability. Different approaches for incorporation of graphene oxide were performed and the cellular adhesion was tested by metabolic assay.
The extracellular matrix (ECM) naturally surrounds cells in humans, and therefore represents the ideal biomaterial for tissue engineering. ECM from different tissues exhibit different composition and physical characteristics. Thus, ECM provides not only physical support but also contains crucial biochemical signals that influence cell adhesion, morphology, proliferation and differentiation. Next to native ECM from mature tissue, ECM can also be obtained from the in vitro culture of cells. In this study, we aimed to highlight the supporting effect of cell-derived- ECM (cdECM) on adipogenic differentiation. ASCs were seeded on top of cdECM from ASCs (scdECM) or pre-adipocytes (acdECM). The impact of ECM on cellular activity was determined by LDH assay, WST I assay and BrdU assay. A supporting effect of cdECM substrates on adipogenic differentiation was determined by oil red O staining and subsequent quantification. Results revealed no effect of cdECM substrates on cellular activity. Regarding adipogenic differentiation a supporting effect of cdECM substrates was obtained compared to control. With these results, we confirm cdECM as a promising biomaterial for adipose tissue engineering.
Increasing number of studies are focused on how adherent cells respond, in vitro, to different properties of a material. Typical properties are the surface chemistry, topographical cues (at the nano- and micro-scale) of the surface, and the substrate stiffness. Cell Response studies are of importance for designing new biomaterials with applications in cell culture technologies, regenerative medicine, or for medical implants. However, only very few studies take the cell age factor, respectively the donor age, into account. In this work, we tested two types of human vascular cells (smooth muscle and endothelial cells) from old and young donors on (a) micro-structured surfaces made of pol (dimethylsiloxane) or on (b) flat polyacrylamide hydrogels with varying stiffnesses. These experiments reveal age-dependent and cell typedependent differences in the cell response to the topography and stiffness, and may establish the Basis for further studies focusing on cell age-dependent responses.
Checklists are a valuable tool to ensure process quality and quality of care. To ensure proper integration in clinical processes, it would be desirable to generate checklists directly from formal process descriptions. Those checklists could also be used for user interaction in context-aware surgical assist systems. We built a tool to automatically convert Business Process Model and Notation (BPMN) process models to checklists displayed as HTML websites. Gateways representing decisions are mapped to checklist items that trigger dynamic content loading based on the placed checkmark. The usability of the resulting system was positively evaluated regarding comprehensibility and end-user friendliness.
This paper presents a modular and scalable power electronics concept for motor control with continuous output voltage. In contrast to multilevel concepts, modules with continuous output voltage are connected in series. The continuous output voltage of each module is obtained by using gallium nitride (GaN) high electron motility transistor (HEMT)s as switches inside the modules with a switching frequency in the range between 500 kHz and 1 MHz. Due to this high switching frequency a LC filter is integrated into the module resulting in a continuous output voltage. A main topic of the paper is the active damping of this LC output filter for each module and the analysis of the series connection of the damping behaviour. The results are illustrated with simulations and measurements.
Size and function of bioartificial tissue models are still limited due to the lack of blood vessels and dynamic perfusion for nutrient supply. In this study, we evaluated the use of cytocompatible methacryl-modified gelatin for the fabrication of a hydrogel-based tube by dip-coating and subsequent photo-initiated cross-linking. The wall thickness of the tubes and the diameter were tuned by the degree of gelatin methacryl-modification and the number of dipping cycles. The dipping temperature of the gelatin solution was adjusted to achieve low viscous fluids of approximately 0.1 Pa s and was different for gelatin derivatives with different modification degrees. A versatile perfusion bioreactor for the supply of surrounding tissue models was developed, which can be adaped to several geometries and sizes of blood-vessel mimicking tubes. The manufactured bendable gelatin tubes were permeable for water and dissolved substances, like Nile Blue and serum albumin. As a proof of concept, human fibroblasts in a three-dimensional collagen tissue model were sucessfully supplied with nutrients via the central gelatin tube under dynamic conditions for 2 days. Moreover, the tubes could be used as scaffolds to build-up a functional and viable endothelial layer. Hence, the presented tools can contribute to solving current challenges in tissue engineering.
This article is a review of the book "Brain computation as hierarchical abstraction" by Dana H. Ballard published by MIT press in 2015. The book series computational neuroscience familiarizes the reader with the computational aspects of brain functions based on neuroscientific evidence. It provides an excellent introduction of the functioning, i.e. the structure, the network and the routines of the brain in our daily life. The final chapters even discuss behavioral elements such as decision-making, emotions and consciousness. These topics are of high relevance in other sciences such as economics and philosophy. Overall, Ballard’s book stimulates a scientifically well-founded debate and, more importantly, reveals the need of an interdisciplinary dialogue towards social sciences.