Fast interactive segmentation of the pulmonary lobes from thoracic computed tomography data

Computer-assisted image-based risk analysis and planning in lung surgery - a review

In this paper, we give an overview on current trends in computer-assisted image-based methods for risk analysis and planning in lung surgery and present our own developments with a focus on computed tomography (CT) based algorithms and applications. The methods combine heuristic, knowledge based image processing algorithms for segmentation, quantification and visualization based on CT images of the lung. Impact for lung surgery is discussed regarding risk assessment, quantitative assessment of resection strategies, and surgical guiding. In perspective, we discuss the role of deep-learning based AI methods for further improvements.

Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies

BACKGROUND

There has been an increasing trend toward pulmonary segmentectomies to treat early-stage lung cancer, small intrapulmonary metastases, and localized benign pathology. A complete preoperative understanding of pulmonary anatomy is essential for accurate surgical planning and case selection. Identifying intersegmental divisions is extremely difficult when performed on computed tomography. For the preoperative planning of segmentectomies, virtual reality (VR) and artificial intelligence could allow 3-dimensional visualization of the complex anatomy of pulmonary segmental divisions, vascular arborization, and bronchial anatomy. This technology can be applied by surgeons preoperatively to gain better insight into a patient's anatomy for planning segmentectomy.

METHODS

In this prospective observational pilot study, we aim to assess and demonstrate the technical feasibility and clinical applicability of the first dedicated artificial intelligence-based and immersive 3-dimensional-VR platform (PulmoVR; jointly developed and manufactured by Department of Cardiothoracic Surgery [Erasmus Medical Center, Rotterdam, The Netherlands], MedicalVR [Amsterdam, The Netherlands], EVOCS Medical Image Communication [Fysicon BV, Oss, The Netherlands], and Thirona [Nijmegen, The Netherlands]) for preoperative planning of video-assisted thoracoscopic segmentectomies.

RESULTS

A total of 10 eligible patients for segmentectomy were included in this study after referral through the institutional thoracic oncology multidisciplinary team. PulmoVR was successfully applied as a supplementary imaging tool to perform video-assisted thoracoscopic segmentectomies. In 40% of the cases, the surgical strategy was adjusted due to the 3-dimensional-VR-based evaluation of anatomy. This underlines the potential benefit of additional VR-guided planning of segmentectomy for both surgeon and patient.

CONCLUSIONS

Our study demonstrates the successful development and clinical application of the first dedicated artificial intelligence and VR platform for the planning of pulmonary segmentectomy. This is the first study that shows an immersive virtual reality-based application for preoperative planning of segmentectomy to the best of our knowledge.

The Emerging Role of Quantification of Imaging for Assessing the Severity and Disease Activity of Emphysema, Airway Disease, and Interstitial Lung Disease

There has been an explosion of use for quantitative image analysis in the setting of lung disease due to advances in acquisition protocols and postprocessing technology, including machine and deep learning. Despite the plethora of published papers, it is important to understand which approach has clinical validation and can be used in clinical practice. This paper provides an introduction to quantitative image analysis techniques being used in the investigation of lung disease and focusses on the techniques that have a reasonable clinical validation for being used in clinical trials and patient care.

Role of Quantitative Computed Tomographic Scan Analysis in Lung Volume Reduction for Emphysema

Recent advances in bronchoscopic lung volume reduction (BLVR) offer new therapeutic alternatives for patients with emphysema and hyperinflation. Endobronchial valves and coils are 2 potential BLVR techniques which have been shown to improve pulmonary function and the quality of life in patients with emphysema. Current patient selection for LVR procedures relies on 3 main inclusion criteria: low attenuation area (in %), also known as emphysema score, heterogeneity score, and fissure integrity score. Volumetric analysis in combination with densitometric analysis of the affected lung lobe or segment with quantitative CT to determine emphysema severity play an important role in treatment planning and post-operative assessment. Due to the variations in lung anatomy, manual corrections are often required to ensure successful and accurate lobe segmentation for pathological and post-treatment CT scan analysis. The advanced development and utilisation of quantitative CT do not simply represent regional changes in pulmonary function but aids in analysis for better patient selection with severe emphysema who are most likely to benefit from BLVR.