Automated Segmentation of Pulmonary Lobes using Coordination-Guided Deep Neural Networks

The identification of pulmonary lobes is of great importance in disease diagnosis and treatment. A few lung diseases have regional disorders at lobar level. Thus, an accurate segmentation of pulmonary lobes is necessary. In this work, we propose an automated segmentation of pulmonary lobes using coordination-guided deep neural networks from chest CT images. We first employ an automated lung segmentation to extract the lung area from CT image, then exploit volumetric convolutional neural network (V-net) for segmenting the pulmonary lobes. To reduce the misclassification of different lobes, we therefore adopt coordination-guided convolutional layers (CoordConvs) that generate additional feature maps of the positional information of pulmonary lobes. The proposed model is trained and evaluated on a few publicly available datasets and has achieved the state-of-the-art accuracy with a mean Dice coefficient index of 0.947 $\pm$ 0.044.Comment: ISBI 2019 (Oral

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.Rudyanto, RD.; Kerkstra, S.; Van Rikxoort, EM.; Fetita, C.; Brillet, P.; Lefevre, C.; Xue, W.... (2014). Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Medical Image Analysis. 18(7):1217-1232. doi:10.1016/j.media.2014.07.003S1217123218

Bronchoscopic lung volume reduction for Emphysema: physiological and radiological correlations

Introduction: Patient selection in lung volume reduction (LVR) plays a pivotal role in achieving meaningful clinical outcomes. Currently, LVR patients are selected based on three established criteria: heterogeneity index, percentage of low attenuation area (%LAA), and fissure integrity score. Quantitative computed tomography (QCT) has been developed to quantify lung physiological indices at the lobar level and could potentially revolutionise patient selection in LVR procedures. We developed an in-house QCT software, LungSeg, and used its radiological indices for the purposes of this thesis. The aim of this thesis is to discover potential physiological and radiological indices that could serve as predictors for superior LVR outcomes for better patient selection. Methods: This thesis took two studies and analysed them using LungSeg. The first study was the long-term coil study, a randomised controlled study that had the control group crossing over to the treatment arm at 12 months. At 12 months post-procedure the baseline measurements were assessed against the 12-months post-procedural measurements. The second study was the short-term valve study which was another randomised controlled study that compared the primary and secondary endpoints between the control and the valve-treated group at three months post-procedure. Results: In the long-term coil study, we found that the best statistically significant combination of predictors for change in target lobar volume at inspiration was found to be the combination of baseline target LV at inspiration, -950HU EI at inspiration, and TLCabs with a model adjusted R2 of 0.407 (p = 0.0001). In a subsequent multivariate analysis using ≥45% LAA on the -950HU at Inspiration, the R2 of the same prediction model did improve to 0.493 (P-value = 0.002). Meanwhile, the best statistically significant combination of predictors for change in target lobar volume at inspiration following valve treatment was found to be the combination of baseline target LV at inspiration, target lobar fissure integrity and baseline FEV1abs with a model adjusted R2 of 0.193 (p = 0.105). Conclusion: Using QCT, we have improved the proposed patient selection algorithm for LVR procedures based on the best QCT and lung function predictors.Open Acces