Search

Abstract: Background: In achalasia, muscle spasm may involve the proximal esophagus. When the muscle spasm is located in the proximal esophagus, conventional per oral endoscopic myotomy (POEM) may not be sufficient to relieve symptoms. In this paper, we describe retrograde endoscopic myotomy (REM) as a novel approach to perform myotomy of the proximal esophagus, with the application of a navigation tool for anatomical guidance during REM. We aim to evaluate the feasibility and safety of REM and usefulness of the navigation during REM. Method: A 42‐year‐old male with type III achalasia who was treated with laparoscopic myotomy with fundoplication, multiple pneumatic balloon dilations, Botox injections and anterior POEM of the middle and distal esophagus without symptomatic effect. Repeated high‐resolution‐ manometry (HRM) revealed occluding contractions of high amplitude around and above the aortic arch. A probe‐based real‐time electromagnetic navigation platform was used to facilitate real‐time anatomical orientation and to evaluate myotomy position and length during REM. Results: The navigation system aided in identifying the major structures of the mediastinum, and position and length of the myotomy. Twelve weeks after REM, the Eckardt score fell from seven at baseline seven to two. We also observed improvement with reduction of the pressure at the level of previous spasms in the proximal esophagus from 124 mmHg to 8 mmHg on HRM. Conclusion: REM makes the proximal esophagus accessible for endoscopic myotomy. Potential indication for REM is motility disorders in the proximal esophagus and therapy failure after POEM. [ABSTRACT FROM AUTHOR]

Knowledge about lung movement in health and disease is sparse. Current evaluation methods, such as CT, MRI and external view have significant limitations. To study respiratory movement for image guided tumour diagnostics and respiratory physiology, we needed a method that overcomes these limitations.We fitted balloon catheters with electromagnetic sensors, and placed them in lung lobes of ventilated pigs. The sensors sensed their position at 40 Hz in an electromagnetic tracking field with a precision of ∼0.5 mm. The method was evaluated by recording sensor movement in different body positions and at different tidal volumes. No 'gold standard' exists for lung segment tracking, so our results were compared to 'common knowledge'. The sensors were easily placed, showed no clinically relevant position drift and yielded sub-millimetre accuracy. Our measurements fit 'common knowledge', as increased ventilation volume increased respiratory movement, and the right lung moved significantly less in the right than the left lateral position. The novel method for tracking lung segment movements during respiration was easy to implement and yielded high spatial and temporal resolution, and the equipment parts are reusable. It is easy to implement as a research tool for lung physiology, navigated bronchoscopy and radiation therapy. [ABSTRACT FROM AUTHOR]

Learning cardiac morphology largely involves spatial abilities and studies indicate benefits from innovative 3D visualization technologies that speed up and increase the learning output. Studies comparing these teaching tools and their educational output are rare and few studies include complex congenital heart defects. This study compared the effects of 3D prints, mixed reality (MR) viewing of 3D meshes and standard cardiac CT slice images on medical students' understanding of complex congenital heart defect morphology, measuring both objective level of understanding and subjective educational experience. The objective of this study was to compare morphological understanding and user experiences of 3D printed models, MR 3D visualization and axial 2D CT slices, in medical students examining morphological details in complex congenital heart defects. Medical students in the median 4th year of study (range 2nd to 6th) examined three of five different complex congenital heart defects by three different modalities: 3D printed model, MR viewed 3D mesh, and cardiac CT slices, answering a questionnaire on morphology and user experience. Time to complete task, diagnostic accuracy, and user experience data were collected and compared on group level. Task times were similar for all modalities. The percentage of correct answers was higher with MR visualization, which was also the preferred modality overall. Medical students both prefer and better understand the morphology of complex congenital heart disease with 3D models viewed using MR, without spending more time than with 3D prints or 2D CT images., (© 2024 The Author(s). Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association for Anatomy.)

To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy. Recently, the Multi-domain Airway Tree Modeling (ATM'22) challenge released a large dataset, both enabling training of deep-learning based models and bringing substantial improvement of the state-of-the-art for the airway segmentation task. The ATM'22 dataset includes a large group of COVID'19 patients and a range of other lung diseases, however, relatively few patients with severe pathologies affecting the airway tree anatomy was found. In this study, we introduce a new public benchmark dataset (AeroPath), consisting of 27 CT images from patients with pathologies ranging from emphysema to large tumors, with corresponding trachea and bronchi annotations. Second, we present a multiscale fusion design for automatic airway segmentation. Models were trained on the ATM'22 dataset, tested on the AeroPath dataset, and further evaluated against competitive open-source methods. The same performance metrics as used in the ATM'22 challenge were used to benchmark the different considered approaches. Lastly, an open web application is developed, to easily test the proposed model on new data. The results demonstrated that our proposed architecture predicted topologically correct segmentations for all the patients included in the AeroPath dataset. The proposed method is robust and able to handle various anomalies, down to at least the fifth airway generation. In addition, the AeroPath dataset, featuring patients with challenging pathologies, will contribute to development of new state-of-the-art methods. The AeroPath dataset and the web application are made openly available., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Støverud et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background and Objective: Patients suspected to have lung cancer, undergo endobronchial ultrasound bronchoscopy (EBUS) for the purpose of diagnosis and staging. For presumptive curable patients, the EBUS bronchoscopy is planned based on images and data from computed tomography (CT) images and positron emission tomography (PET). Our study aimed to evaluate the feasibility of a multimodal electromagnetic navigation platform for EBUS bronchoscopy, integrating ultrasound and segmented CT, and PET scan imaging data., Methods: The proof-of-concept study included patients with suspected lung cancer and pathological mediastinal/hilar lymph nodes identified on both CT and PET scans. Images obtained from these two modalities were segmented to delineate target lymph nodes and then incorporated into the CustusX navigation platform. The EBUS bronchoscope was equipped with a sensor, calibrated, and affixed to a 3D printed click-on device positioned at the bronchoscope's tip. Navigation accuracy was measured postoperatively using ultrasound recordings., Results: The study enrolled three patients, all presenting with suspected mediastinal lymph node metastasis (N1-3). All PET-positive lymph nodes were displayed in the navigation platform during the EBUS procedures. In total, five distinct lymph nodes were sampled, yielding malignant cells from three nodes and lymphocytes from the remaining two. The median accuracy of the navigation system was 7.7 mm., Conclusion: Our study introduces a feasible multimodal electromagnetic navigation platform that combines intraoperative ultrasound with preoperative segmented CT and PET imaging data for EBUS lymph node staging examinations. This innovative approach holds promise for enhancing the accuracy and effectiveness of EBUS procedures., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Kildahl-Andersen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Endobronchial ultrasound (EBUS) is used in the minimally invasive sampling of thoracic lymph nodes. In lung cancer staging, the accurate assessment of mediastinal structures is essential but challenged by variations in anatomy, image quality, and operator-dependent image interpretation. This study aimed to automatically detect and segment mediastinal lymph nodes and blood vessels employing a novel U-Net architecture-based approach in EBUS images. A total of 1161 EBUS images from 40 patients were annotated. For training and validation, 882 images from 30 patients and 145 images from 5 patients were utilized. A separate set of 134 images was reserved for testing. For lymph node and blood vessel segmentation, the mean ± standard deviation (SD) values of the Dice similarity coefficient were 0.71 ± 0.35 and 0.76 ± 0.38, those of the precision were 0.69 ± 0.36 and 0.82 ± 0.22, those of the sensitivity were 0.71 ± 0.38 and 0.80 ± 0.25, those of the specificity were 0.98 ± 0.02 and 0.99 ± 0.01, and those of the F1 score were 0.85 ± 0.16 and 0.81 ± 0.21, respectively. The average processing and segmentation run-time per image was 55 ± 1 ms (mean ± SD). The new U-Net architecture-based approach (EBUS-AI) could automatically detect and segment mediastinal lymph nodes and blood vessels in EBUS images. The method performed well and was feasible and fast, enabling real-time automatic labeling., Competing Interests: Øyvind Ervik reports one lecture fee from MSD. Hanne Sorger reports one lecture fee from AstraZeneca. For the remaining authors, there are no conflicts of interest or other disclosures.

Machine learning and deep learning are novel methods which are revolutionizing medical imaging. In our study we trained an algorithm with a U-Net shaped network to recognize ultrasound images of the median nerve in the complete distal half of the forearm and to measure the cross-sectional area at the inlet of the carpal tunnel. Images of 25 patient hands with carpal tunnel syndrome (CTS) and 26 healthy controls were recorded on a video loop covering 15 cm of the distal forearm and 2355 images were manually segmented. We found an average Dice score of 0.76 between manual and automated segmentation of the median nerve in its complete course, while the measurement of the cross-sectional area at the carpal tunnel inlet resulted in a 10.9% difference between manually and automated measurements. We regard this technology as a suitable device for verifying the diagnosis of CTS., (© 2024. The Author(s).)

Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images., Methods: A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dental clinicians, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models., Results: The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively., Conclusion: Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images., (© 2024. The Author(s).)

The purpose of this study was to evaluate the accuracy of a method for estimating the tip position of a fiber optic shape-sensing (FOSS) integrated instrument being inserted through a bronchoscope. A modified guidewire with a multicore optical fiber was inserted into the working channel of a custom-made catheter with three electromagnetic (EM) sensors. The displacement between the instruments was manually set, and a point-based method was applied to match the position of the EM sensors to corresponding points on the shape. The accuracy was evaluated in a realistic bronchial model. An additional EM sensor was used to sample the tip of the guidewire, and the absolute deviation between this position and the estimated tip position was calculated. For small displacements between the tip of the FOSS integrated tool and the catheter, the median deviation in estimated tip position was ≤5 mm. For larger displacements, deviations exceeding 10 mm were observed. The deviations increased when the shape sensor had sharp curvatures relative to more straight shapes. The method works well for clinically relevant displacements of a biopsy tool from the bronchoscope tip, and when the path to the lesion has limited curvatures. However, improvements must be made to our configuration before pursuing further clinical testing., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction and aims: During an Endovascular Aneurysm Repair (EVAR) procedure a stiff guidewire is inserted from the iliac arteries. This induces significant deformations on the vasculature, thus, affecting the pre-operative planning, and the accuracy of image fusion. The aim of the present work is to predict the guidewire induced deformations using a finite element approach validated through experiments with patient-specific additive manufactured models. The numerical approach herein developed could improve the pre-operative planning and the intra-operative navigation. Material and methods: The physical models used for the experiments in the hybrid operating room, were manufactured from the segmentations of pre-operative Computed Tomography (CT) angiographies. The finite element analyses (FEA) were performed with LS-DYNA Explicit. The material properties used in finite element analyses were obtained by uniaxial tensile tests. The experimental deformed configurations of the aorta were compared to those obtained from FEA. Three models, obtained from Computed Tomography acquisitions, were investigated in the present work: A) without intraluminal thrombus (ILT), B) with ILT, C) with ILT and calcifications. Results and discussion: A good agreement was found between the experimental and the computational studies. The average error between the final in vitro vs. in silico aortic configurations, i.e., when the guidewire is fully inserted, are equal to 1.17, 1.22 and 1.40 mm, respectively, for Models A, B and C. The increasing trend in values of deformations from Model A to Model C was noticed both experimentally and numerically. The presented validated computational approach in combination with a tracking technology of the endovascular devices may be used to obtain the intra-operative configuration of the vessels and devices prior to the procedure, thus limiting the radiation exposure and the contrast agent dose., Competing Interests: Author PD was employed by HSL S.r.l. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Emendi, Støverud, Tangen, Ulsaker, Manstad-H, Di Giovanni, Dahl, Langø and Prot.)

Purpose: This study aims to explore training strategies to improve convolutional neural network-based image-to-image deformable registration for abdominal imaging., Methods: Different training strategies, loss functions, and transfer learning schemes were considered. Furthermore, an augmentation layer which generates artificial training image pairs on-the-fly was proposed, in addition to a loss layer that enables dynamic loss weighting., Results: Guiding registration using segmentations in the training step proved beneficial for deep-learning-based image registration. Finetuning the pretrained model from the brain MRI dataset to the abdominal CT dataset further improved performance on the latter application, removing the need for a large dataset to yield satisfactory performance. Dynamic loss weighting also marginally improved performance, all without impacting inference runtime., Conclusion: Using simple concepts, we improved the performance of a commonly used deep image registration architecture, VoxelMorph. In future work, our framework, DDMR, should be validated on different datasets to further assess its value., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 de Frutos et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Purpose: Despite extensive preoperative imaging, intraoperative localization of liver lesions after systemic treatment can be challenging. Therefore, an image-guided navigation setup is explored that links preoperative diagnostic scans and 3D models to intraoperative ultrasound (US), enabling overlay of detailed diagnostic images on intraoperative US. Aim of this study is to assess the workflow and accuracy of such a navigation system which compensates for liver motion., Methods: Electromagnetic (EM) tracking was used for organ tracking and movement of the transducer. After laparotomy, a sensor was attached to the liver surface while the EM-tracked US transducer enabled image acquisition and landmark digitization. Landmarks surrounding the lesion were selected during patient-specific preoperative 3D planning and identified for registration during surgery. Endpoints were accuracy and additional times of the investigative steps. Accuracy was computed at the center of the target lesion., Results: In total, 22 navigated procedures were performed. Navigation provided useful visualization of preoperative 3D models and their overlay on US imaging. Landmark-based registration resulted in a mean fiducial registration error of 10.3 ± 4.3 mm, and a mean target registration error of 8.5 ± 4.2 mm. Navigation was available after an average of 12.7 min., Conclusion: We developed a navigation method combining ultrasound with active liver tracking for organ motion compensation, with an accuracy below 10 mm. Fixation of the liver sensor near the target lesion compensates for local movement and contributes to improved reliability during navigation. This represents an important step forward in providing surgical navigation throughout the procedure., Trial Registration: This study is registered in the Netherlands Trial Register (number NL7951)., (© 2022. CARS.)

Introduction: The established method for assessment of mediastinal and hilar lymph nodes is endobronchial ultrasound bronchoscopy (EBUS) with needle aspirations. Previously, we presented an electromagnetic navigation platform for this purpose. There were several issues with the permanent electromagnetic tracking (EMT) sensor attachment on the tip of the experimental EBUS bronchoscope. The purpose was to develop a device for on-site attachment of the EMT sensor., Material and Methods: A clip-on EMT sensor attachment device was 3D-printed in Ultem™ and attached to an EBUS bronchoscope. A specially designed ultrasound probe calibration adapter was developed for on-site and quick probe calibration. Navigation accuracy was studied using a wire cross water phantom and clinical feasibility was tested in a healthy volunteer., Results: The device attached to the EBUS bronchoscope increased its diameter from 6.9 mm to 9.5 mm. Average preclinical navigation accuracy was 3.9 mm after adapter calibration. The maneuvering of the bronchoscope examining a healthy volunteer was adequate without harming the respiratory epithelium, and the device stayed firmly attached., Conclusion: Development, calibration and testing of a clip-on EMT sensor attachment device for EBUS bronchoscopy was successfully demonstrated. Acceptable accuracy results were obtained, and the device is ready to be tested in patient studies.

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Purpose: Cancer is among the leading causes of death in the developed world, and lung cancer is the most lethal type. Early detection is crucial for better prognosis, but can be resource intensive to achieve. Automating tasks such as lung tumor localization and segmentation in radiological images can free valuable time for radiologists and other clinical personnel. Convolutional neural networks may be suited for such tasks, but require substantial amounts of labeled data to train. Obtaining labeled data is a challenge, especially in the medical domain., Methods: This paper investigates the use of a teacher-student design to utilize datasets with different types of supervision to train an automatic model performing pulmonary tumor segmentation on computed tomography images. The framework consists of two models: the student that performs end-to-end automatic tumor segmentation and the teacher that supplies the student additional pseudo-annotated data during training., Results: Using only a small proportion of semantically labeled data and a large number of bounding box annotated data, we achieved competitive performance using a teacher-student design. Models trained on larger amounts of semantic annotations did not perform better than those trained on teacher-annotated data. Our model trained on a small number of semantically labeled data achieved a mean dice similarity coefficient of 71.0 on the MSD Lung dataset., Conclusions: Our results demonstrate the potential of utilizing teacher-student designs to reduce the annotation load, as less supervised annotation schemes may be performed, without any real degradation in segmentation accuracy., Competing Interests: The authors have declared that no competing interests exist.

Introduction: Effectiveness of e-learning diminishes without the support of a pedagogical model to guide its use. In minimally invasive surgery (MIS), this has been reported as a limitation when technology is used to deliver contents without a sound pedagogical background., Material and Methods: We describe how a generic pedagogical model, the 3D pedagogy framework, can be used for setting learning outcomes and activities in e-learning platforms focused on MIS cognitive skills. A demonstrator course on Nissen fundoplication was developed following the model step-by-step in the MISTELA learning platform. Course design was informed by Kolb's Experiential learning model. Content validation was performed by 13 MIS experts., Results: Ten experts agreed on the suitability of content structuring done according to the pedagogical model. All experts agreed that the course provides means to assess the intended learning outcomes., Conclusions: This work showcases how a general-purpose e-learning framework can be accommodated to the needs of MIS training without limiting the course designers' pedagogical approach. Key advances for its success include: (1) proving the validity of the model in the wider scope of MIS skills and (2) raising awareness amongst stakeholders on the need of developing training plans with explicit, rather than assumed, pedagogical foundations. Abbreviations: MIS: minimally invasive surgery; TEL: technology enhanced learning.

Purpose: To present a novel methodical approach to compare visibility of percutaneous needles in ultrasound images., Methods: A motor-driven rotation platform was used to gradually change the needle angle while capturing image data. Data analysis was automated using block-matching-based registration, with a tracking and refinement step. Every 25 frames, a Hough transform was used to improve needle alignments after large rotations. The method was demonstrated by comparing three commercial needles (14G radiofrequency ablation, RFA; 18G Trocar; 22G Chiba) and six prototype needles with different sizes, materials, and surface conditions (polished, sand-blasted, and kerfed), within polyvinyl alcohol phantom tissue and ex vivo bovine liver models. For each needle and angle, a contrast-to-noise ratio (CNR) was determined to quantify visibility. CNR values are presented as a function of needle type and insertion angle. In addition, the normalized area under the (CNR-angle) curve was used as a summary metric to compare needles., Results: In phantom tissue, the first kerfed needle design had the largest normalized area of visibility and the polished 1 mm diameter stainless steel needle the smallest (0.704 ± 0.199 vs. 0.154 ± 0.027, p < 0.01). In the ex vivo model, the second kerfed needle design had the largest normalized area of visibility, and the sand-blasted stainless steel needle the smallest (0.470 ± 0.190 vs. 0.127 ± 0.047, p < 0.001). As expected, the analysis showed needle visibility peaks at orthogonal insertion angles. For acute or obtuse angles, needle visibility was similar or reduced. Overall, the variability in needle visibility was considerably higher in livers., Conclusion: The best overall visibility was found with kerfed needles and the commercial RFA needle. The presented methodical approach to quantify ultrasound visibility allows comparisons of (echogenic) needles, as well as other technological innovations aiming to improve ultrasound visibility of percutaneous needles, such as coatings, material treatments, and beam steering approaches., (© 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Extracorporeal shock wave lithotripsy (ESWL) of kidney stones is losing ground to more expensive and invasive endoscopic treatments., Objective: This proof-of-concept project was initiated to develop artificial intelligence (AI)-augmented ESWL and to investigate the potential for machine learning to improve the efficacy of ESWL., Design Setting and Participants: Two-dimensional ultrasound videos were captured during ESWL treatments from an inline ultrasound device with a video grabber. An observer annotated 23 212 images from 11 patients as either in or out of focus. The median hit rate was calculated on a patient level via bootstrapping. A convolutional neural network with U-Net architecture was trained on 57 ultrasound images with delineated kidney stones from the same patients annotated by a second observer. We tested U-Net on the ultrasound images annotated by the first observer. Cross-validation with a training set of nine patients, a validation set of one patient, and a test set of one patient was performed., Outcome Measurements and Statistical Analysis: Classical metrics describing classifier performance were calculated, together with an estimation of how the algorithm would affect shock wave hit rate., Results and Limitations: The median hit rate for standard ESWL was 55.2% (95% confidence interval [CI] 43.2-67.3%). The performance metrics for U-Net were accuracy 63.9%, sensitivity 56.0%, specificity 74.7%, positive predictive value 75.3%, negative predictive value 55.2%, Youden's J statistic 30.7%, no-information rate 58.0%, and Cohen's κ 0.2931. The algorithm reduced total mishits by 67.1%. The main limitation is that this is a proof-of-concept study involving only 11 patients., Conclusions: Our calculated ESWL hit rate of 55.2% (95% CI 43.2-67.3%) supports findings from earlier research. We have demonstrated that a machine learning algorithm trained on just 11 patients increases the hit rate to 75.3% and reduces mishits by 67.1%. When U-Net is trained on more and higher-quality annotations, even better results can be expected., Patient Summary: Kidney stones can be treated by applying shockwaves to the outside of the body. Ultrasound scans of the kidney are used to guide the machine delivering the shockwaves, but the shockwaves can still miss the stone. We used artificial intelligence to improve the accuracy in hitting the stone being treated., (© 2021 The Author(s).)

Objectives: The goal was to demonstrate the utility of open-source tracking and visualisation tools in the targeting of lung cancer. Material and methods: The study demonstrates the first deployment of the Anser electromagnetic (EM) tracking system with the CustusX image-guided interventional research platform to navigate using an endobronchial catheter to injected tumour targets. Live animal investigations validated the deployment and targeting of peripheral tumour models using an innovative tumour marking routine. Results: Novel tumour model deployment was successfully achieved at all eight target sites across two live animal investigations without pneumothorax. Virtual bronchoscopy with tracking successfully guided the tracked catheter to 2-12 mm from the target tumour site. Deployment of a novel marker was achieved at all eight sites providing a reliable measure of targeting accuracy. Targeting accuracy within 10 mm was achieved in 7/8 sites and in all cases, the virtual target distance at marker deployment was within the range subsequently measured with x-ray.C onclusions: Endobronchial targeting of peripheral airway targets is feasible using existing open-source technology. Notwithstanding the shortcomings of current commercial platforms, technological improvements in EM tracking and registration accuracy fostered by open-source technology may provide the impetus for widespread clinical uptake of electromagnetic navigation in bronchoscopy.

Purpose: Electromagnetic tracking is a core platform technology in the navigation and visualisation of image-guided procedures. The technology provides high tracking accuracy in non-line-of-sight environments, allowing instrument navigation in locations where optical tracking is not feasible. EMT can be beneficial in applications such as percutaneous radiofrequency ablation for the treatment of hepatic lesions where the needle tip may be obscured due to difficult liver environments (e.g subcutaneous fat or ablation artefacts). Advances in the field of EMT include novel methods of improving tracking system accuracy, precision and error compensation capabilities, though such system-level improvements cannot be readily incorporated in current therapy applications due to the 'blackbox' nature of commercial tracking solving algorithms., Methods: This paper defines a software framework to allow novel EMT designs, and improvements become part of the global design process for image-guided interventions. An exemplary framework is implemented in the Python programming language and demonstrated with the open-source Anser EMT system. The framework is applied in the preclinical setting though targeted liver ablation therapy on an animal model., Results: The developed framework was tested with the Anser EMT electromagnetic tracking platform. Liver tumour targeting was performed using the tracking framework with the CustusX navigation platform using commercially available electromagnetically tracked needles. Ablation of two tumours was performed with a commercially available ablation system. Necropsy of the tumours indicated ablations within 5 mm of the tumours., Conclusions: An open-source framework for electromagnetic tracking was presented and effectively demonstrated in the preclinical setting. We believe that this framework provides a structure for future advancement in EMT system in and customised instrument design.

Purpose: Accurate lung cancer diagnosis is crucial to select the best course of action for treating the patient. From a simple chest CT volume, it is necessary to identify whether the cancer has spread to nearby lymph nodes or not. It is equally important to know precisely where each malignant lymph node is with respect to the surrounding anatomical structures and the airways. In this paper, we introduce a new data-set containing annotations of fifteen different anatomical structures in the mediastinal area, including lymph nodes of varying sizes. We present a 2D pipeline for semantic segmentation and instance detection of anatomical structures and potentially malignant lymph nodes in the mediastinal area., Methods: We propose a 2D pipeline combining the strengths of U-Net for pixel-wise segmentation using a loss function dealing with data imbalance and Mask R-CNN providing instance detection and improved pixel-wise segmentation within bounding boxes. A final stage performs pixel-wise labels refinement and 3D instance detection using a tracking approach along the slicing dimension. Detected instances are represented by a 3D pixel-wise mask, bounding volume, and centroid position., Results: We validated our approach following a fivefold cross-validation over our new data-set of fifteen lung cancer patients. For the semantic segmentation task, we reach an average Dice score of 76% over all fifteen anatomical structures. For the lymph node instance detection task, we reach 75% recall for 9 false positives per patient, with an average centroid position estimation error of 3 mm in each dimension., Conclusion: Fusing 2D networks' results increases pixel-wise segmentation results while enabling good instance detection. Better leveraging of the 3D information and station mapping for the detected lymph nodes are the next steps.

During ultrasound-guided percutaneous interventions, needle localization can be a challenge. To increase needle visibility, enhancements of both the imaging methods and the needle surface properties have been investigated. However, a methodical approach to compare potential solutions is currently unavailable. The work described here involves automated image acquisition, analysis and reporting techniques to collect large amounts of data efficiently, delineate relevant factors and communicate effects. Data processing included filtering, line fitting and image intensity analysis steps. Foreground and background image samples were used to compute a contrast-to-noise ratio or a signal ratio. The approach was evaluated in a comparative study of commercially available and custom-made needles. Varied parameters included needle material, diameter and surface roughness. The shafts with kerfed patterns and the trocar and chiba tips performed best. The approach enabled an intuitive polar depiction of needle visibility in ultrasound images for a large range of insertion angles., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: The aim of this paper is to show how a specialized planning and guidance application called Fraxinus, can be built on top of the CustusX platform (www.custusx.org), which is an open source image-guided intervention software platform. Fraxinus has been customized to meet the clinical needs in navigated bronchoscopy., Methods: The application requirements for Fraxinus were defined in close collaboration between research scientists, software developers and clinicians (pulmonologists), and built on top of CustusX. Its superbuild system downloads specific versions of the required libraries and builds them for the application in question, including the selected plugins. New functionality is easily added through the plugin framework. The build process enables the creation of specialized applications, adding additional documentation and custom configurations. The toolkit's libraries offer building blocks for image-guided applications. An iterative development process was applied, where the clinicians would test and provide feedback during the entire process., Results: Fraxinus has been developed and is released as an open source planning and guidance application built on top of CustusX. It is highly specialized for bronchoscopy. The proposed workflow is adapted to the different steps in this procedure. The user interface of CustusX has been modified to enhance information, quality assurance and user friendliness with the intention to increase the overall yield for the patient. As the workflow of the procedure is relatively constant, some actions are predicted and automatically performed by the application, according to the requirements from the clinicians., Conclusions: The CustusX platform facilitates development of new and specialized applications. The toolkit supports the process and makes important extension and injection points available for customization., Competing Interests: The authors have declared that no competing interests exist.

Objective: Endoluminal visualization in virtual and video bronchoscopy lacks information about the surrounding structures, and the traditional 2 D axial, coronal and sagittal CT views can be difficult to interpret. To address this challenge, we previously introduced a novel visualization technique, Anchored to Centerline Curved Surface, for navigated bronchoscopy. The current study compares the ACCuSurf to the standard ACS CT views as planning and guiding tools in a phantom study., Material and Methods: Bronchoscope operators navigated in physical phantom guided by virtual realistic image data constructed by fusion of CT dataset of phantom and anonymized patient CT data. We marked four different target positions within the virtual image data and gave 12 pulmonologists the task to navigate, with either ACCuSurf or ACS as guidance, to the corresponding targets in the physical phantom., Results: Using ACCuSurf reduced the planning time and increased the grade of successful navigation significantly compared to ACS., Conclusion: The phantom setup with virtual patient image data proved realistic according to the pulmonologists. ACCuSurf proved superior to ACS regarding planning time and navigation success grading. Improvements on visualisation or display techniques may consequently improve both planning and navigated bronchoscopy and thus contribute to more precise lung diagnostics.

Purpose: Test the feasibility of the novel Single Landmark image-to-patient registration method for use in the operating room for future clinical trials. The algorithm is implemented in the open-source platform CustusX, a computer-aided intervention research platform dedicated to intraoperative navigation and ultrasound, with an interface for laparoscopic ultrasound probes., Methods: The Single Landmark method is compared to fiducial landmark on an IOUSFAN (Kyoto Kagaku Co., Ltd., Japan) soft tissue abdominal phantom and T2 magnetic resonance scans of it., Results: The experiments show that the accuracy of the Single Landmark registration is good close to the registered point, increasing with the distance from this point (12.4 mm error at 60 mm away from the registered point). In this point, the registration accuracy is mainly dominated by the accuracy of the user when clicking on the ultrasound image. In the presented set-up, the time required to perform the Single Landmark registration is 40% less than for the FLRM., Conclusion: The Single Landmark registration is suitable for being integrated in a laparoscopic workflow. The statistical analysis shows robustness against translational displacements of the patient and improvements in terms of time. The proposed method allows the clinician to accurately register lesions intraoperatively by clicking on these in the ultrasound image provided by the ultrasound transducer. The Single Landmark registration method can be further combined with other more accurate registration approaches improving the registration at relevant points defined by the clinicians.

Competing Interests: Competing interests: None declared.

Objective: In flexible endoscopy techniques, such as bronchoscopy, there is often a challenge visualizing the path from start to target based on preoperative data and accessing these during the procedure. An example of this is visualizing only the inside of central airways in bronchoscopy. Virtual bronchoscopy (VB) does not meet the pulmonologist's need to detect, define and sample the frequent targets outside the bronchial wall. Our aim was to develop and study a new visualization technique for navigated bronchoscopy., Material and Methods: We extracted the shortest possible path from the top of the trachea to the target along the airway centerline and a corresponding auxiliary route in the opposite lung. A surface structure between the centerlines was developed and displayed. The new technique was tested on non-selective CT data from eight patients using artificial lung targets., Results: The new display technique anchored to centerline curved surface (ACCuSurf) made it easy to detect and interpret anatomical features, targets and neighboring anatomy outside the airways, in all eight patients., Conclusions: ACCuSurf can simplify planning and performing navigated bronchoscopy, meets the challenge of improving orientation and register the direction of the moving endoscope, thus creating an optimal visualization for navigated bronchoscopy.

Purpose: The objective of this study was to develop a multimodal, permanent liver phantom displaying functional vasculature and common pathologies, for teaching, training and equipment development in laparoscopic ultrasound and navigation., Methods: Molten wax was injected simultaneously into the portal and hepatic veins of a human liver. Upon solidification of the wax, the surrounding liver tissue was dissolved, leaving a cast of the vessels. A connection was established between the two vascular trees by manually manipulating the wax. The cast was placed, along with different multimodal tumor models, in a liver shaped mold, which was subsequently filled with a polymer. After curing, the wax was melted and flushed out of the model, thereby establishing a system of interconnected channels, replicating the major vasculature of the original liver. Thus, a liquid can be circulated through the model in a way that closely mimics the natural blood flow., Results: Both the tumor models, i.e., the metastatic tumors, hepatocellular carcinoma and benign cyst, and the vessels inside the liver model, were clearly visualized by all the three imaging modalities: CT, MR and ultrasound. Doppler ultrasound images of the vessels proved the blood flow functionality of the phantom., Conclusion: By a two-step casting procedure, we produced a multimodal liver phantom, with open vascular channels, and tumor models, that is the next best thing to practicing imaging and guidance procedures in animals or humans. The technique is in principle applicable to any organ of the body.

Background and Objective: Virtual reality (VR) simulators enrich surgical training and offer training possibilities outside of the operating room (OR). In this study, we created a criterion-based training program on a VR simulator with haptic feedback and tested it by comparing the performances of a simulator group against a control group., Material and Methods: Medical students with no experience in laparoscopy were randomly assigned to a simulator group or a control group. In the simulator group, the candidates trained until they reached predefined criteria on the LapSim ® VR simulator (Surgical Science AB, Göteborg, Sweden) with haptic feedback (Xitact TM IHP, Mentice AB, Göteborg, Sweden). All candidates performed a cholecystectomy on a porcine organ model in a box trainer (the clinical setting). The performances were video rated by two surgeons blinded to subject training status., Results: In total, 30 students performed the cholecystectomy and had their videos rated (N = 16 simulator group, N = 14 control group). The control group achieved better video rating scores than the simulator group (p < .05)., Conclusions: The criterion-based training program did not transfer skills to the clinical setting. Poor mechanical performance of the simulated haptic feedback is believed to have resulted in a negative training effect.