Airway segmentation for low-contrast CT images from combined PET/CT scanners based on airway modelling and seed prediction

Assessing tumor centrality in lung stereotactic ablative body radiotherapy (SABR): the effects of variations in bronchial tree delineation and potential for automated methods

Accurate delineation of the proximal bronchial tree (PBT) is crucial for appropriate assessment of lung tumor centrality and choice of Stereotactic Ablative Body Radiotherapy (SABR) dose prescription. Here, we investigate variabilities in manual PBT delineation and their potential to influence assessing lesion centrality. A fully automatic, intensity-based tool for PBT contouring and measuring distance to the target is also described. This retrospective analysis included a total of 61 patients treated with lung SABR. A subset of 41 patients was used as a training dataset, containing clinical PBT contour and additional subsequently generated manual contours. The tool was optimized and compared against manual contours in terms of volume, distance to the target and various overlap/similarity metrics. The remaining 20 patients were used as a validation dataset to investigate the dosimetric effects of variations between manual and automatic PBT contours. Considerable interobserver variability was observed, particularly in identifying the superior and inferior borders of the PBT. Automatic PBT contours were comparable to manual contours with average Dice of 0.63 to 0.79 and mean distance to agreement of 1.78 to 3.34 mm. No significant differences in dosimetric parameters were found between automatically and manually generated contours. A moderate negative correlation was found between PBT maximum dose and distance to the lesion (p < 0.05). Variability in manual PBT delineation may result in inconsistent assessment of tumor centrality. Automatic contouring can help standardize clinical practice, support investigations into the link between SABR outcomes and lesion proximity to central airways and the development of predictive toxicity models that incorporate precise measurements of tumor location in relation to high-risk organs.

Review of automatic pulmonary lobe segmentation methods from CT

The computational detection of pulmonary lobes from CT images is a challenging segmentation problem with important respiratory health care applications, including surgical planning and regional image analysis. Several authors have proposed automated algorithms and we present a methodological review. These algorithms share a number of common stages and we consider each stage in turn, comparing the methods applied by each author and discussing their relative strengths. No standard method has yet emerged and none of the published methods have been demonstrated across a full range of clinical pathologies and imaging protocols. We discuss how improved methods could be developed by combining different approaches, and we use this to propose a workflow for the development of new algorithms.

Fusion of lung MR/CT images through lung vessel registration

Lung perfusion analysis with sequential contrasted MRI is an important clinical tool. This work is part of larger research in which the objective of fusing lung perfusion analysis and lung anatomical structures. In this work, it is proposed a multimodality MR/CT lung fusion algorithm based on lung vessel determination that analyzes lung perfusion. 3D contrasted MR and 3D CT images are first normalized (slice thickness, resolution and pixel intensity), then lung features are determined and elastically registered. The registration algorithm was checked by mapping in both directions, from MR to CT and vice-versa. A MR perfusion analysis result is fused with a 3D CT segmented lung vessel tree.