Segmentation of distal airways using structural analysis

Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution

Pulmonary Vascular Tree Segmentation from Contrast-Enhanced CT Images

We present a pulmonary vessel segmentation algorithm, which is fast, fully automatic and robust. It uses a coarse segmentation of the airway tree and a left and right lung labeled volume to restrict a vessel enhancement filter, based on an offset medialness function, to the lungs. We show the application of our algorithm on contrast-enhanced CT images, where we derive a clinical parameter to detect pulmonary hypertension (PH) in patients. Results on a dataset of 24 patients show that quantitative indices derived from the segmentation are applicable to distinguish patients with and without PH. Further work-in-progress results are shown on the VESSEL12 challenge dataset, which is composed of non-contrast-enhanced scans, where we range in the midfield of participating contestants.Comment: Part of the OAGM/AAPR 2013 proceedings (1304.1876

Development and validation of HRCT airway segmentation algorithms

Direct measurements of airway lumen and wall areas are potentially useful as a diagnostic tool and as an aid to understanding the pathophysiology underlying lung disease. Direct measurements can be made from images created by high resolution computer tomography (HRCT) by using computer-based algorithms to segment airways, but current validation techniques cannot adequately establish the accuracy and precision of these algorithms. A detailed review of HRCT airway segmentation algorithms was undertaken, from which three candidate algorithm designs were developed. A custom Windows-based software program was implemented to facilitate multi-modality development and validation of the segmentation algorithms. The performance of the algorithms was examined in clinical HRCT images. A centre-likelihood (CL) ray-casting algorithm was found to be the most suitable algorithm due to its speed and reliability in semi-automatic segmentation and tracking of the airway wall. Several novel refinements were demonstrated to improve the CL algorithm’s robustness in HRCT lung data. The performance of the CL algorithm was then quantified in two-dimensional simulated data to optimise customisable parameters such as edge-detection method, interpolation and number of rays. Novel correction equations to counter the effects of volume averaging and airway orientation angle were derived and demonstrated in three-dimensional simulated data. The optimal CL algorithm was validated with HRCT data using a plastic phantom and a pig lung phantom matched to micro-CT. Accuracy was found to be improved compared to previous studies using similar methods. The volume averaging correction was found to improve precision and accuracy in the plastic phantom but not in the pig lung phantom. When tested in a clinical setting the results of the optimised CL algorithm was in agreement with the results of other measures of lung function. The thesis concludes that the relative contributions of confounders of airway measurement have been quantified in simulated data and the CL algorithm’s performance has been validated in a plastic phantom as well as animal model. This validation protocol has improved the accuracy and precision of measurements made using the CL algorith

Pulmonary Lobe Segmentation with Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior

A fully automated, unsupervised lobe segmentation algorithm is presented based on a probabilistic segmentation of the fissures and the simultaneous construction of a population model of the fissures. A two-class probabilistic segmentation segments the lung into candidate fissure voxels and the surrounding parenchyma. This was combined with anatomical information and a groupwise fissure prior to drive non-parametric surface fitting to obtain the final segmentation. The performance of our fissure segmentation was validated on 30 patients from the COPDGene cohort, achieving a high median F1-score of 0:90 and showed general insensitivity to filter parameters. We evaluated our lobe segmentation algorithm on the LOLA11 dataset, which contains 55 cases at varying levels of pathology. We achieved the highest score of 0:884 of the automated algorithms. Our method was further tested quantitatively and qualitatively on 80 patients from the COPDGene study at varying levels of functional impairment. Accurate segmentation of the lobes is shown at various degrees of fissure incompleteness for 96% of all cases. We also show the utility of including a groupwise prior in segmenting the lobes in regions of grossly incomplete fissures

New insights on COPD imaging via CT and MRI

Multidetector-row computed tomography (MDCT) can be used to quantify morphological features and investigate structure/function relationship in COPD. This approach allows a phenotypical definition of COPD patients, and might improve our understanding of disease pathogenesis and suggest new therapeutical options. In recent years, magnetic resonance imaging (MRI) has also become potentially suitable for the assessment of ventilation, perfusion and respiratory mechanics. This review focuses on the established clinical applications of CT, and novel CT and MRI techniques, which may prove valuable in evaluating the structural and functional damage in COPD

Impact of emphysema heterogeneity on pulmonary function

Results: The majority (128/160) of the subjects with COPD had a heterogeneity greater than zero. After adjusting for age, gender, smoking history, and extent of emphysema, heterogeneity in depicted disease in upper lobe dominant cases was positively associated with pulmonary function measures, such as FEV1 Predicted (p<.001) and FEV1/FVC (p<.001), as well as disease severity (p<0.05). We found a negative association between HI% , RV/TLC (p<0.001), and DLco% (albeit not a statistically significant one, p = 0.06) in this group of patients

Quantifying Airway Dilatation in the Lungs from Computed Tomography

Non CF bronchiectasis and idiopathic pulmonary fibrosis (IPF) are pulmonary diseases characterised by the abnormal and permanent dilatation of the airways. Computed tomography (CT) is used in clinical practice to diagnose and monitor patients with the disease. Currently, analysis of the scans is performed by manual inspection and there is no established computerised method to quantify the enlargement of airways. I developed a pipeline to quantify the cross-sectional area for a given airway track. Using an airway segmentation, my proposed algorithm measures the area at contiguous intervals along the airway arclength from the Carina to the most distal point visible on CT. I showed the use of the data generated from the pipeline in two applications. First, I proposed a novel tapering measure as the gradient of a linear regression between a logarithmic area against the arclength. The measurement was applied to airways affected by bronchiectasis. Second, I used Bayesian Changepoint Detection (BCD) with the area measurements to locate the progression of IPF along the airway track. The proposed pipeline was applied to a set of clinically acquired scans. I show a statistical difference (p = 3.4×10−4 ) in the tapering measurement between bronchiectatic (n = 53) and controlled (n = 39) airways. In addition, I report a statistical difference (p = 7.2×10−3 ) in the change in measurement between airways remaining healthy (n = 14) and airways that have become bronchiectatic (n = 5). I show the tapering measurement is reproducible independent to voxel size, CT reconstruction, and radiation dose. Using BCD, I show on simulated data (n = 14) my proposed method can detect the progression of IPF within 2.5mm. Finally, using results from BCD, I present a novel measure of IPF progression as the percentage volume change in the diseased region of the airways

Cardiovascular and Thoracic Imaging: Trends, Perspectives and Prospects

Radiology is evolving at a fast pace, and the specific field of cardiovascular and thoracic imaging is no stranger to that trend. While it could, at first, seem unusual to gather these two specialties in a common Issue, the very fact that many of us are trained and exercise in both is more than a hint to the common grounds these fields are sharing. From the ever-increasing role of artificial intelligence in the reconstruction, segmentation, and analysis of images to the quest of functionality derived from anatomy, their interplay is big, and one innovation developed with the former in mind could prove useful for the latter. If the coronavirus disease 2019 (COVID-19) pandemic has shed light on the decisive diagnostic role of chest CT and, to a lesser extent, cardiac MR, one must not forget the major advances and extensive researches made possible in other areas by these techniques in the past years. With this Issue, we aim at encouraging and wish to bring to light state-of-the-art reviews, novel original researches, and ongoing discussions on the multiple aspects of cardiovascular and chest imaging