Mixed Reality Biopsy Navigation System Utilizing Markerless Needle Tracking and Imaging Data Superimposition.

Exact biopsy planning and careful execution of needle injection is crucial to ensure successful procedure completion as initially intended while minimizing the risk of complications. This study introduces a solution aimed at helping the operator navigate to precisely position the needle in a previously planned trajectory utilizing a mixed reality headset. A markerless needle tracking method was developed by integrating deep learning and deterministic computer vision techniques. The system is based on superimposing imaging data onto the patient's body in order to directly perceive the anatomy and determine a path from the selected injection site to the target location. Four types of tests were conducted to assess the system's performance: measuring the accuracy of needle pose estimation, determining the distance between injection sites and designated targets, evaluating the efficiency of material collection, and comparing procedure time and number of punctures required with and without the system. These tests, involving both phantoms and physician participation in the latter two, demonstrated the accuracy and usability of the proposed solution. The results showcased a significant improvement, with a reduction in number of punctures needed to reach the target location. The test was successfully completed on the first attempt in 70% of cases, as opposed to only 20% without the system. Additionally, there was a 53% reduction in procedure time, validating the effectiveness of the system.

Using a novel three-dimensional holographic technology to perform open vascular surgery procedures.

Augmented reality technology has been introduced during recent years into everyday clinical practice. Several surgical specialties have begun using such technology for preoperative planning as well as intraoperatively. Regarding vascular surgery, a limited number of reports have described the benefits, mainly for endovascular procedures. We aim to present a novel three-dimensional holographic system we used to perform an open vascular procedure.

Feasibility and accuracy of real-time 3D-holographic graft length measurements.

Aims: Mixed reality (MR) holograms can display high-definition images while preserving the user's situational awareness. New MR software can measure 3D objects with gestures and voice commands; however, these measurements have not been validated. We aimed to assess the feasibility and accuracy of using 3D holograms for measuring the length of coronary artery bypass grafts. Methods and results: An independent core lab analyzed follow-up computer tomography coronary angiograms performed 30 days after coronary artery bypass grafting in 30 consecutive cases enrolled in the FASTTRACK CABG trial. Two analysts, blinded to clinical information, performed holographic reconstruction and measurements using the CarnaLife Holo software (Medapp, Krakow, Poland). Inter-observer agreement was assessed in the first 20 cases. Another analyst performed the validation measurements using the CardIQ W8 CT system (GE Healthcare, Milwaukee, Wisconsin). Seventy grafts (30 left internal mammary artery grafts, 31 saphenous vein grafts, and 9 right internal mammary artery grafts) were measured. Holographic measurements were feasible in 97.1% of grafts and took 3 minutes 36 s ± 50.74 s per case. There was an excellent inter-observer agreement [interclass correlation coefficient (ICC) 0.99 (0.97-0.99)]. There was no significant difference between the total graft length on hologram and CT [187.5 mm (157.7-211.4) vs. 183.1 mm (156.8-206.1), P = 0.50], respectively. Hologram and CT measurements are highly correlated (r = 0.97, P < 0.001) with an excellent agreement [ICC 0.98 (0.97-0.99)]. Conclusion: Real-time holographic measurements are feasible, quick, and accurate even for tortuous bypass grafts. This new methodology can empower clinicians to visualize and measure 3D images by themselves and may provide insights for procedural strategy.

Comparison between Micro-Powder Injection Molding and Material Extrusion Additive Manufacturing of Metal Powders for the Fabrication of Sintered Components.

Original compositions based on iron micro-powders and an organic binder mixture were developed for the fabrication of sintered metallic elements with micro-powder injection molding (µPIM) and material extrusion additive manufacturing of metal powders (MEX). The binder formulation was thoroughly adjusted to exhibit rheological and thermal properties suitable for µPIM and MEX. The focus was set on adapting the proper binder composition to meet the requirements for injection/extrusion and, at the same time, to have comparable thermogravimetric characteristics for the thermal debinding and sintering process. A basic analysis of the forming process indicates that the pressure has a low influence on clogging, while the temperature of the material and mold/nozzle impacts the viscosity of the composition significantly. The influence of the Fe micro-powder content in the range of 45-60 vol.% was evaluated against the injection/extrusion process parameters and properties of sintered elements. Different debinding and sintering processes (chemical and thermal) were evaluated for the optimal properties of the final samples. The obtained sintered elements were of high quality and showed minor signs of binder-related flaws, with shrinkage in the range of 10-15% for both the injection-molded and 3D printed parts. These results suggest that, with minor modifications, compositions tailored for the PIM technique can be adapted for the additive manufacturing of metal parts, achieving comparable characteristics of the parts obtained for both forming methods.

DRU-Net: Pulmonary Artery Segmentation via Dense Residual U-Network with Hybrid Loss Function.

The structure and topology of the pulmonary arteries is crucial to understand, plan, and conduct medical treatment in the thorax area. Due to the complex anatomy of the pulmonary vessels, it is not easy to distinguish between the arteries and veins. The pulmonary arteries have a complex structure with an irregular shape and adjacent tissues, which makes automatic segmentation a challenging task. A deep neural network is required to segment the topological structure of the pulmonary artery. Therefore, in this study, a Dense Residual U-Net with a hybrid loss function is proposed. The network is trained on augmented Computed Tomography volumes to improve the performance of the network and prevent overfitting. Moreover, the hybrid loss function is implemented to improve the performance of the network. The results show an improvement in the Dice and HD95 scores over state-of-the-art techniques. The average scores achieved for the Dice and HD95 scores are 0.8775 and 4.2624 mm, respectively. The proposed method will support physicians in the challenging task of preoperative planning of thoracic surgery, where the correct assessment of the arteries is crucial.

Deep learning-based framework for automatic cranial defect reconstruction and implant modeling.

BACKGROUND AND OBJECTIVE: This article presents a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling. METHODS: We propose a two-step deep learning-based method using a modified U-Net architecture to perform the defect reconstruction, and a dedicated iterative procedure to improve the implant geometry, followed by an automatic generation of models ready for 3-D printing. We propose a cross-case augmentation based on imperfect image registration combining cases from different datasets. Additional ablation studies compare different augmentation strategies and other state-of-the-art methods. RESULTS: We evaluate the method on three datasets introduced during the AutoImplant 2021 challenge, organized jointly with the MICCAI conference. We perform the quantitative evaluation using the Dice and boundary Dice coefficients, and the Hausdorff distance. The Dice coefficient, boundary Dice coefficient, and the 95th percentile of Hausdorff distance averaged across all test sets, are 0.91, 0.94, and 1.53 mm respectively. We perform an additional qualitative evaluation by 3-D printing and visualization in mixed reality to confirm the implant's usefulness. CONCLUSION: The article proposes a complete pipeline that enables one to create the cranial implant model ready for 3-D printing. The described method is a greatly extended version of the method that scored 1st place in all AutoImplant 2021 challenge tasks. We freely release the source code, which together with the open datasets, makes the results fully reproducible. The automatic reconstruction of cranial defects may enable manufacturing personalized implants in a significantly shorter time, possibly allowing one to perform the 3-D printing process directly during a given intervention. Moreover, we show the usability of the defect reconstruction in a mixed reality that may further reduce the surgery time.

The Use of Mixed Reality in Custom-Made Revision Hip Arthroplasty: A First Case Report.

The technology of 3D printing and visualization of anatomical structures is rapidly growing in various fields of medicine. A custom-made implant and mixed reality were used to perform complex revision hip arthroplasty in January 2019. The use of mixed reality allowed for a very good visualization of the structures and resulted in precise implant fixation. According to the authors' knowledge, this is the first described case report of the combined use of these two innovations. The diagnosis preceding the qualification for the procedure was the loosening of the left hip's acetabular component. Mixed reality headset and holograms prepared by engineers were used during the surgery. The operation was successful, and it was followed by early verticalization and patient rehabilitation. The team sees opportunities for technology development in joint arthroplasty, trauma, and orthopedic oncology.

CNT/Graphite/SBS Conductive Fibers for Strain Sensing in Wearable Telerehabilitation Devices.

Rapid growth of personal electronics with concurrent research into telerehabilitation solutions discovers opportunities to redefine the future of orthopedic rehabilitation. After joint injury or operation, convalescence includes free active range of movement exercises, such as joints bending and straightening under medical supervision. Flexion detection through wearable textile sensors provides numerous potential benefits such as: (1) reduced cost; (2) continuous monitoring; (3) remote telerehabilitation; (4) gamification; and (5) detection of risk-inducing activities in daily routine. To address this issue, novel piezoresistive multi-walled carbon nanotubes/graphite/styrene-butadiene-styrene copolymer (CNT/Gr/SBS) fiber was developed. The extrusion process allowed adjustable diameter fiber production, while being a scalable, industrially adapted method of manufacturing textile electronics. Composite fibers were highly stretchable, withstanding strains up to 285%, and exhibited exceptional piezoresistive parameters with a gauge factor of 91.64 for 0-100% strain range and 2955 for the full scope. Considering the composite's flexibility and sensitivity during a series of cyclic loading, it was concluded that developed Gr/CNT/SBS fibers were suitable for application in wearable piezoresistive sensors for telerehabilitation application.

3D mixed-reality visualization of medical imaging data as a supporting tool for innovative, minimally invasive surgery for gastrointestinal tumors and systemic treatment as a new path in personalized treatment of advanced cancer diseases.

BACKGROUND: The purpose of this study was to investigate the potential of a combination of 3D mixed-reality visualization of medical images using CarnaLife Holo (MedApp, Poland) system as a supporting tool for innovative, minimally invasive surgery/irreversible electroporation-IRA, Nano-Knife), microwave ablation (MWA)/for advanced gastrointestinal tumors. Eight liver and pancreatic tumor treatments were performed. In all of the patients undergoing laparoscopy or open surgery volume and margin were estimated by preoperative visualization. In all patients, neoplastic lesions were considered unresectable by standard methods. METHODS: Preoperative CT or MRI were transformed into holograms and displayed thanks to the HoloLens 2. During operation, the surgeon's field of view was augmented with a 3D model of the patient's relevant structures. RESULTS: The intraoperative hologram contributed to better presentation of tumor size and locations, more precise setting of needles used to irreversible electroporation and for determining ablation line in case of liver metastases. Surgeons could easily compare the real patient's anatomy to holographic visualization just before the operations. CONCLUSIONS: The combination of 3D mixed-reality visualization using CarnaLife Holo with IRA, MWA and next systemic treatment (chemotherapy) might be a new way in personalized treatment of advanced cancers.

Soldering of Electronics Components on 3D-Printed Conductive Substrates.

Rapid development of additive manufacturing and new composites materials with unique properties are promising tools for fabricating structural electronics. However, according to the typical maximum resolution of additive manufacturing methods, there is no possibility to fabricate all electrical components with these techniques. One way to produce complex structural electronic circuits is to merge 3D-printed elements with standard electronic components. Here, different soldering and surface preparation methods before soldering are tested to find the optimal method for soldering typical electronic components on conductive, 3D-printed, composite substrates. To determine the optimal soldering condition, the contact angles of solder joints fabricated in different conditions were measured. Additionally, the mechanical strength of the joints was measured using the shear force test. The research shows a possibility of fabricating strong, conductive solder joints on composites substrates prepared by additive manufacturing. The results show that mechanical cleaning and using additional flux on the composite substrates are necessary to obtain high-quality solder joints. The most repeatable joints with the highest shear strength values were obtained using reflow soldering together with low-temperature SnBiAg solder alloy. A fabricated demonstrator is a sample of the successful merging of 3D-printed structural electronics with standard electronic components.

Semi-Supervised Deep Learning-Based Image Registration Method with Volume Penalty for Real-Time Breast Tumor Bed Localization.

Breast-conserving surgery requires supportive radiotherapy to prevent cancer recurrence. However, the task of localizing the tumor bed to be irradiated is not trivial. The automatic image registration could significantly aid the tumor bed localization and lower the radiation dose delivered to the surrounding healthy tissues. This study proposes a novel image registration method dedicated to breast tumor bed localization addressing the problem of missing data due to tumor resection that may be applied to real-time radiotherapy planning. We propose a deep learning-based nonrigid image registration method based on a modified U-Net architecture. The algorithm works simultaneously on several image resolutions to handle large deformations. Moreover, we propose a dedicated volume penalty that introduces the medical knowledge about tumor resection into the registration process. The proposed method may be useful for improving real-time radiation therapy planning after the tumor resection and, thus, lower the surrounding healthy tissues' irradiation. The data used in this study consist of 30 computed tomography scans acquired in patients with diagnosed breast cancer, before and after tumor surgery. The method is evaluated using the target registration error between manually annotated landmarks, the ratio of tumor volume, and the subjective visual assessment. We compare the proposed method to several other approaches and show that both the multilevel approach and the volume regularization improve the registration results. The mean target registration error is below 6.5 mm, and the relative volume ratio is close to zero. The registration time below 1 s enables the real-time processing. These results show improvements compared to the classical, iterative methods or other learning-based approaches that do not introduce the knowledge about tumor resection into the registration process. In future research, we plan to propose a method dedicated to automatic localization of missing regions that may be used to automatically segment tumors in the source image and scars in the target image.

Multistep, automatic and nonrigid image registration method for histology samples acquired using multiple stains.

The use of multiple dyes during histological sample preparation can reveal distinct tissue properties. However, since the slide preparation differs for each dye, the tissue slides are being deformed and a nonrigid registration is required before further processing. The registration of histology images is complicated because of: (i) a high resolution of histology images, (ii) complex, large, nonrigid deformations, (iii) difference in the appearance and partially missing data due to the use of multiple dyes. In this work, we propose a multistep, automatic, nonrigid image registration method dedicated to histology samples acquired with multiple stains. The proposed method consists of a feature-based affine registration, an exhaustive rotation alignment, an iterative, intensity-based affine registration, and a nonrigid alignment based on modality independent neighbourhood descriptor coupled with the Demons algorithm. A dedicated failure detection mechanism is proposed to make the method fully automatic, without the necessity of any manual interaction. The described method was proposed by the AGH team during the Automatic Non-rigid Histological Image Registration (ANHIR) challenge. The ANHIR dataset consists of 481 image pairs annotated by histology experts. Moreover, the ANHIR challenge submissions were evaluated using an independent, server-side evaluation tool. The main evaluation criteria was the target registration error normalized by the image diagonal. The median of median target registration error is below 0.19%. The proposed method is currently the second-best in terms of the average ranking of median target registration error, without statistically significant differences compared to the top-ranked method. We provide an open access to the method software and used parameters, making the results fully reproducible.

Automatic Quality Assessment of Reflectance Confocal Microscopy Mosaics using Attention-Based Deep Neural Network.

Skin cancers are the most common cancers with an increased incidence, and a valid, early diagnosis may significantly reduce its morbidity and mortality. Reflectance confocal microscopy (RCM) is a relatively new, non-invasive imaging technique that allows screening lesions at a cellular resolution. However, one of the main disadvantages of the RCM is frequently occurring artifacts which makes the diagnostic process more time consuming and hard to automate using e.g. end-to-end deep learning approach. A tool to automatically determine the RCM mosaic quality could be beneficial for both the lesion classification and informing the user (dermatologist) about its quality in real-time, during the examination procedure. In this work, we propose an attention-based deep network to automatically determine if a given RCM mosaic has an acceptable quality. We achieved accuracy above 87% on the test set which may considerably improve further classification results and the RCM-based examination.

Contact-Free Multispectral Identity Verification System Using Palm Veins and Deep Neural Network.

Devices and systems secured by biometric factors became a part of our lives because they are convenient, easy to use, reliable, and secure. They use information about unique features of our bodies in order to authenticate a user. It is possible to enhance the security of these devices by adding supplementary modality while keeping the user experience at the same level. Palm vein systems are based on infrared wavelengths used for capturing images of users' veins. It is both convenient for the user, and it is one of the most secure biometric solutions. The proposed system uses IR and UV wavelengths; the images are then processed by a deep convolutional neural network for extraction of biometric features and authentication of users. We tested the system in a verification scenario that consisted of checking if the images collected from the user contained the same biometric features as those in the database. The True Positive Rate (TPR) achieved by the system when the information from the two modalities were combined was 99.5% by the threshold of acceptance set to the Equal Error Rate (EER).

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.

Highly Conductive Carbon Nanotube-Thermoplastic Polyurethane Nanocomposite for Smart Clothing Applications and Beyond.

The following paper presents a simple, inexpensive and scalable method of production of carbon nanotube-polyurethane elastomer composite. The new method enables the formation of fibers with 40% w/w of nanotubes in a polymer. Thanks to the 8 times higher content of nanotubes than previously reported for such composites, over an order of magnitude higher electrical conductivity is also observed. The composite fibers are highly elastic and both their electrical and mechanical properties may be easily controlled by changing the nanotubes content in the composite. It is shown that these composite fibers may be easily integrated with traditional textiles by sewing or ironing. However, taking into account their light-weight, high conductivity, flexibility and easiness of molding it may be expected that their potential applications are not limited to the smart textiles industry.

Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification.

This study presents an approach to Parkinson's disease detection using vowels with sustained phonation and a ResNet architecture dedicated originally to image classification. We calculated spectrum of the audio recordings and used them as an image input to the ResNet architecture pre-trained using the ImageNet and SVD databases. To prevent overfitting the dataset was strongly augmented in the time domain. The Parkinson's dataset (from PC-GITA database) consists of 100 patients (50 were healthy / 50 were diagnosed with Parkinson's disease). Each patient was recorded 3 times. The obtained accuracy on the validation set is above 90% which is comparable to the current state-of-the-art methods. The results are promising because it turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal. What is more, we showed that it is possible to perform a successful detection of Parkinson's disease using only frequency-based features. A spectrogram enables visual representation of frequencies spectrum of a signal. It allows to follow the frequencies changes of a signal in time.

Convolutional Neural Network Approach to Classify Skin Lesions Using Reflectance Confocal Microscopy.

We propose an approach based on a convolutional neural network to classify skin lesions using the reflectance confocal microscopy (RCM) mosaics. Skin cancers are the most common type of cancers and a correct, early diagnosis significantly lowers both morbidity and mortality. RCM is an in-vivo non-invasive screening tool that produces virtual biopsies of skin lesions but its proficient and safe use requires hard to obtain expertise. Therefore, it may be useful to have an additional tool to aid diagnosis. The proposed network is based on the ResNet architecture. The dataset consists of 429 RCM mosaics and is divided into 3 classes: melanoma, basal cell carcinoma, and benign naevi with the ground-truth confirmed by a histopathological examination. The test set classification accuracy was 87%, higher than the accuracy achieved by medical, confocal users. The results show that the proposed classification system can be a useful tool to aid in early, noninvasive melanoma detection.

Efficient Inkjet Printing of Graphene-Based Elements: Influence of Dispersing Agent on Ink Viscosity.

Inkjet printing is an excellent printing technique and an attractive alternative to conventional technologies for the production of flexible, low-cost microelectronic devices. Among many parameters that have a significant impact on the correctness of the printing process, the most important is ink viscosity. During the printing process, the ink is influenced by different strains and forces, which significantly change the printing results. The authors present a model and calculations referring to the shear rate of ink in an inkjet printer nozzle. Supporting experiments were conducted, proving the model assumptions for two different ink formulations: initial ink and with the addition of a dispersing agent. The most important findings are summarized by the process window regime of parameters, which is much broader for the inks with a dispersing agent. Such inks exhibit preferable viscosity, better print-ability, and higher path quality with lower resistivity. Presented results allow stating that proper, stable graphene inks adjusted for inkjet technique rheology must contain modifiers such as dispersing agents to be effectively printed.

LEFMIS: locally-oriented evaluation framework for medical image segmentation algorithms.

This article proposes a novel framework for the locally-oriented evaluation of segmentation algorithms (LEFMIS). The presented approach is robust and takes into account local inter/intra-observer variability and the anisotropy of medical images. What is more, the framework makes it possible to distinguish types of error locally. These features are crucial in the context of cancer image data. The proposed framework is based on use of the signed anisotropic Euclidean distance transform and the distance projection. It can be used easily in many different applications with or without additional expert outlines (both inter- and intra-observer variability). The performance of the proposed framework is depicted using both artificial and kidney cancer CT data with experts' manual outlines. In the article, in the case of artificial data, it is presented that the manual outlines dispersion is symmetric in relation to the truth border. The effectiveness of the selected segmentation algorithm was analysed in the context of kidney cancer using computed tomography data. For the calculated local inter-observer variability, 80.11% of the surface points generated by the kidney segmentation algorithm are within one expert outline standard deviation and 97.96% are within five. An error distribution shift in the direction of type I error equivalent was also observed. Finally, the significance of the local estimation of error type differences is presented. The article shows the greater usefulness and flexibility of the proposed framework in comparison to the state-of-the-art methods. The exemplary usage of the LEFMIS with or without inter-/intra-observer variability is also presented.