Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding

As lung cancer evolves, the presence of potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. A method for accurate and automatic segmentation is hence decisive for quantitatively describing lymph nodes. In this study, the use of 3D convolutional neural networks, either through slab-wise schemes or the leveraging of downsampled entire volumes, is investigated. As lymph nodes have similar attenuation values to nearby anatomical structures, we use the knowledge of other organs as prior information to guide the segmentation. To assess the performances, a 5-fold cross-validation strategy was followed over a dataset of 120 contrast-enhanced CT volumes. For the 1178 lymph nodes with a short-axis diameter ≥10 mm, our best-performing approach reached a patient-wise recall of 92%, a false positive per patient ratio of 5 and a segmentation overlap of 80.5%. Fusing a slab-wise and a full volume approach within an ensemble scheme generated the best performances. The anatomical priors guiding strategy is promising, yet a larger set than four organs appears needed to generate an optimal benefit. A larger dataset is also mandatory given the wide range of expressions a lymph node can exhibit (i.e. shape, location and attenuation).publishedVersio

AeroPath: An airway segmentation benchmark dataset with challenging pathology

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. However, the ATM'22 dataset includes few patients with severe pathologies affecting the airway tree anatomy. 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.Comment: 13 pages, 5 figures, submitted to Scientific Report

Bronchoscopy using a head-mounted mixed reality device—a phantom study and a first in-patient user experience

Background: Bronchoscopy for peripheral lung lesions may involve image sources such as computed tomography (CT), fluoroscopy, radial endobronchial ultrasound (R-EBUS), and virtual/electromagnetic navigation bronchoscopy. Our objective was to evaluate the feasibility of replacing these multiple monitors with a head-mounted display (HMD), always providing relevant image data in the line of sight of the bronchoscopist.Methods: A total of 17 pulmonologists wearing a HMD (Microsoft® HoloLens 2) performed bronchoscopy with electromagnetic navigation in a lung phantom. The bronchoscopists first conducted an endobronchial inspection and navigation to the target, followed by an endobronchial ultrasound bronchoscopy. The HMD experience was evaluated using a questionnaire. Finally, the HMD was used in bronchoscopy inspection and electromagnetic navigation of two patients presenting with hemoptysis.Results: In the phantom study, the perceived quality of video and ultrasound images was assessed using a visual analog scale, with 100% representing optimal image quality. The score for video quality was 58% (95% confidence interval [CI] 48%–68%) and for ultrasound image quality, the score was 43% (95% CI 30%–56%). Contrast, color rendering, and resolution were all considered suboptimal. Despite adjusting the brightness settings, video image rendering was considered too dark. Navigation to the target for biopsy sampling was accomplished by all participants, with no significant difference in procedure time between experienced and less experienced bronchoscopists. The overall system latency for the image stream was 0.33–0.35 s. Fifteen of the pulmonologists would consider using HoloLens for navigation in the periphery, and two would not consider using HoloLens in bronchoscopy at all. In the human study, bronchoscopy inspection was feasible for both patients.Conclusion: Bronchoscopy using an HMD was feasible in a lung phantom and in two patients. Video and ultrasound image quality was considered inferior to that of video monitors. HoloLens 2 was suboptimal for airway and mucosa inspection but may be adequate for virtual bronchoscopy navigation

An open electromagnetic tracking framework applied to targeted liver tumour ablation

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

A new procedure for automatic path planning in bronchoscopy

Virtual bronchoscopy is often used for planning a real bronchoscopy procedure. Software applications are developed for virtual bronchoscopy, involving usually segmentation of the tracheobronchial tree from the medical image scan, which is a difficult operation, both conceptually and from the computer implementation and running time point of view. That is why in this paper, a new method for bronchoscopy procedure planning that does not require such a segmentation is presented. The proposed procedure involves automatic path generation between the starting and ending points, skin removal, an algorithm for detection and resolution of collision with the airways walls and validation of the automatically created path. Results are presented for two datasets – one being the representation of a theoretical lungs model, with six levels of branches and the other one being the image scan of a real patient. Together with a system for tracking the bronchoscope during the real procedure, the proposed method can improve the diagnostic success rate of lung cancer using bronchoscopy and decrease the discomfort perceived by the patient.acceptedVersio

Accuracy of electromagnetic tracking with a prototype field generator in an interventional OR setting

Purpose: The authors have studied the accuracy and robustness of a prototype electromagnetic window field generator (WFG) in an interventional radiology suite with a robotic C-arm. The overall purpose is the development of guidance systems combining real-time imaging with tracking of flexible instruments for bronchoscopy, laparoscopic ultrasound, endoluminal surgery, endovascular therapy, and spinal surgery. Methods: The WFG has a torus shape, which facilitates x-ray imaging through its centre. The authors compared the performance of the WFG to that of a standard field generator (SFG) under the influence of the C-arm. Both accuracy and robustness measurements were performed with the C-arm in different positions and poses. Results: The system was deemed robust for both field generators, but the accuracy was notably influenced as the C-arm was moved into the electromagnetic field. The SFG provided a smaller root-mean-square position error but was more influenced by the C-arm than the WFG. The WFG also produced smaller maximum and variance of the error. Conclusions: Electromagnetic (EM) tracking with the new WFG during C-arm based fluoroscopy guidance seems to be a step forward, and with a correction scheme implemented it should be feasible

A new procedure for automatic path planning in bronchoscopy

Virtual bronchoscopy is often used for planning a real bronchoscopy procedure. Software applications are developed for virtual bronchoscopy, involving usually segmentation of the tracheobronchial tree from the medical image scan, which is a difficult operation, both conceptually and from the computer implementation and running time point of view. That is why in this paper, a new method for bronchoscopy procedure planning that does not require such a segmentation is presented. The proposed procedure involves automatic path generation between the starting and ending points, skin removal, an algorithm for detection and resolution of collision with the airways walls and validation of the automatically created path. Results are presented for two datasets – one being the representation of a theoretical lungs model, with six levels of branches and the other one being the image scan of a real patient. Together with a system for tracking the bronchoscope during the real procedure, the proposed method can improve the diagnostic success rate of lung cancer using bronchoscopy and decrease the discomfort perceived by the patient

Pulmonologist evaluation on new CT visualization for guidance to lung lesions during bronchoscopy

Translator disclaimer Full Article Figures & data References Citations Metrics Reprints & Permissions Get access Abstract 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.acceptedVersio

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Patient 02 - CT and Ultrasoun