Automatic lung segmentation and quantification of aeration in computed tomography of the chest using 3D transfer learning

Background: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. Methods: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2NetPig) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (SJI and SBF, respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. Results: On the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = −0.2 {95% conf. int. −0.23 : −0.16} and SBF = −0.1 {−0.5 : −0.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = −0.29 {−0.36 : −0.22} and SBF = −0.43 {−0.54 : −0.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P < 0.001, but reduced robustness SJI = −0.46 {−0.52 : −0.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = −0.48 {−0.59 : −0.36}. u2NetTransfer improved JI compared to u2NetHuman in segmenting healthy (P = 0.008), ARDS (P < 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2NetTransfer segmentations exhibited < 5% volume difference compared to MS. Conclusion: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability

Distribution of transpulmonary pressure during one-lung ventilation in pigs at different body positions

Background. Global and regional transpulmonary pressure (PL) during one-lung ventilation (OLV) is poorly characterized. We hypothesized that global and regional PL and driving PL (ΔPL) increase during protective low tidal volume OLV compared to two-lung ventilation (TLV), and vary with body position.Methods. In sixteen anesthetized juvenile pigs, intra-pleural pressure sensors were placed in ventral, dorsal, and caudal zones of the left hemithorax by video-assisted thoracoscopy. A right thoracotomy was performed and lipopolysaccharide administered intravenously to mimic the inflammatory response due to thoracic surgery. Animals were ventilated in a volume-controlled mode with a tidal volume (VT) of 6 mL kg−1 during TLV and of 5 mL kg−1 during OLV and a positive end-expiratory pressure (PEEP) of 5 cmH2O. Global and local transpulmonary pressures were calculated. Lung instability was defined as end-expiratory PL&lt;2.9 cmH2O according to previous investigations. Variables were acquired during TLV (TLVsupine), left lung ventilation in supine (OLVsupine), semilateral (OLVsemilateral), lateral (OLVlateral) and prone (OLVprone) positions randomized according to Latin-square sequence. Effects of position were tested using repeated measures ANOVA.Results. End-expiratory PL and ΔPL were higher during OLVsupine than TLVsupine. During OLV, regional end-inspiratory PL and ΔPL did not differ significantly among body positions. Yet, end-expiratory PL was lower in semilateral (ventral: 4.8 ± 2.9 cmH2O; caudal: 3.1 ± 2.6 cmH2O) and lateral (ventral: 1.9 ± 3.3 cmH2O; caudal: 2.7 ± 1.7 cmH2O) compared to supine (ventral: 4.8 ± 2.9 cmH2O; caudal: 3.1 ± 2.6 cmH2O) and prone position (ventral: 1.7 ± 2.5 cmH2O; caudal: 3.3 ± 1.6 cmH2O), mainly in ventral (p ≤ 0.001) and caudal (p = 0.007) regions. Lung instability was detected more often in semilateral (26 out of 48 measurements; p = 0.012) and lateral (29 out of 48 measurements, p &lt; 0.001) as compared to supine position (15 out of 48 measurements), and more often in lateral as compared to prone position (19 out of 48 measurements, p = 0.027).Conclusion. Compared to TLV, OLV increased lung stress. Body position did not affect stress of the ventilated lung during OLV, but lung stability was lowest in semilateral and lateral decubitus position

Automatic Lung Segmentation and Quantification of Aeration in Computed Tomography of the Chest Using 3D Transfer Learning

Background: Identification of lung parenchyma on computer tomographic (CT) scans in the research setting is done semi-automatically and requires cumbersome manual correction. This is especially true in pathological conditions, hindering the clinical application of aeration compartment (AC) analysis. Deep learning based algorithms have lately been shown to be reliable and time-efficient in segmenting pathologic lungs. In this contribution, we thus propose a novel 3D transfer learning based approach to quantify lung volumes, aeration compartments and lung recruitability. Methods: Two convolutional neural networks developed for biomedical image segmentation (uNet), with different resolutions and fields of view, were implemented using Matlab. Training and evaluation was done on 180 scans of 18 pigs in experimental ARDS (u2NetPig) and on a clinical data set of 150 scans from 58 ICU patients with lung conditions varying from healthy, to COPD, to ARDS and COVID-19 (u2NetHuman). One manual segmentations (MS) was available for each scan, being a consensus by two experts. Transfer learning was then applied to train u2NetPig on the clinical data set generating u2NetTransfer. General segmentation quality was quantified using the Jaccard index (JI) and the Boundary Function score (BF). The slope between JI or BF and relative volume of non-aerated compartment (SJI and SBF, respectively) was calculated over data sets to assess robustness toward non-aerated lung regions. Additionally, the relative volume of ACs and lung volumes (LV) were compared between automatic and MS. Results: On the experimental data set, u2NetPig resulted in JI = 0.892 [0.88 : 091] (median [inter-quartile range]), BF = 0.995 [0.98 : 1.0] and slopes SJI = 120.2 {95% conf. int. 120.23 : 120.16} and SBF = 120.1 { 120.5 : 120.06}. u2NetHuman showed similar performance compared to u2NetPig in JI, BF but with reduced robustness SJI = 120.29 { 120.36 : 120.22} and SBF = 120.43 { 120.54 : 120.31}. Transfer learning improved overall JI = 0.92 [0.88 : 0.94], P &lt; 0.001, but reduced robustness SJI = 120.46 { 120.52 : 120.40}, and affected neither BF = 0.96 [0.91 : 0.98] nor SBF = 120.48 { 120.59 : 120.36}. u2NetTransfer improved JI compared to u2NetHuman in segmenting healthy (P = 0.008), ARDS (P &lt; 0.001) and COPD (P = 0.004) patients but not in COVID-19 patients (P = 0.298). ACs and LV determined using u2NetTransfer segmentations exhibited &lt; 5% volume difference compared to MS. Conclusion: Compared to manual segmentations, automatic uNet based 3D lung segmentation provides acceptable quality for both clinical and scientific purposes in the quantification of lung volumes, aeration compartments, and recruitability

Regional lung aeration and ventilation during pressure support and biphasic positive airway pressure ventilation in experimental lung injury

INTRODUCTION: There is an increasing interest in biphasic positive airway pressure with spontaneous breathing (BIPAP+SBmean), which is a combination of time-cycled controlled breaths at two levels of continuous positive airway pressure (BIPAP+SBcontrolled) and non-assisted spontaneous breathing (BIPAP+SBspont), in the early phase of acute lung injury (ALI). However, pressure support ventilation (PSV) remains the most commonly used mode of assisted ventilation. To date, the effects of BIPAP+SBmean and PSV on regional lung aeration and ventilation during ALI are only poorly defined. METHODS: In 10 anesthetized juvenile pigs, ALI was induced by surfactant depletion. BIPAP+SBmean and PSV were performed in a random sequence (1 h each) at comparable mean airway pressures and minute volumes. Gas exchange, hemodynamics, and inspiratory effort were determined and dynamic computed tomography scans obtained. Aeration and ventilation were calculated in four zones along the ventral-dorsal axis at lung apex, hilum and base. RESULTS: Compared to PSV, BIPAP+SBmean resulted in: 1) lower mean tidal volume, comparable oxygenation and hemodynamics, and increased PaCO2 and inspiratory effort; 2) less nonaerated areas at end-expiration; 3) decreased tidal hyperaeration and re-aeration; 4) similar distributions of ventilation. During BIPAP+SBmean: i) BIPAP+SBspont had lower tidal volumes and higher rates than BIPAP+SBcontrolled; ii) BIPAP+SBspont and BIPAP+SBcontrolled had similar distributions of ventilation and aeration; iii) BIPAP+SBcontrolled resulted in increased tidal re-aeration and hyperareation, compared to PSV. BIPAP+SBspont showed an opposite pattern. CONCLUSIONS: In this model of ALI, the reduction of tidal re-aeration and hyperaeration during BIPAP+SBmean compared to PSV is not due to decreased nonaerated areas at end-expiration or different distribution of ventilation, but to lower tidal volumes during BIPAP+SBspont. The ratio between spontaneous to controlled breaths seems to play a pivotal role in reducing tidal re-aeration and hyperaeration during BIPAP+SBmean

Rationale and study design of PROVHILO - a worldwide multicenter randomized controlled trial on protective ventilation during general anesthesia for open abdominal surgery

<p>Abstract</p> <p>Background</p> <p>Post-operative pulmonary complications add to the morbidity and mortality of surgical patients, in particular after general anesthesia >2 hours for abdominal surgery. Whether a protective mechanical ventilation strategy with higher levels of positive end-expiratory pressure (PEEP) and repeated recruitment maneuvers; the "open lung strategy", protects against post-operative pulmonary complications is uncertain. The present study aims at comparing a protective mechanical ventilation strategy with a conventional mechanical ventilation strategy during general anesthesia for abdominal non-laparoscopic surgery.</p> <p>Methods</p> <p>The PROtective Ventilation using HIgh versus LOw positive end-expiratory pressure ("PROVHILO") trial is a worldwide investigator-initiated multicenter randomized controlled two-arm study. Nine hundred patients scheduled for non-laparoscopic abdominal surgery at high or intermediate risk for post-operative pulmonary complications are randomized to mechanical ventilation with the level of PEEP at 12 cmH₂O with recruitment maneuvers (the lung-protective strategy) or mechanical ventilation with the level of PEEP at maximum 2 cmH₂O without recruitment maneuvers (the conventional strategy). The primary endpoint is any post-operative pulmonary complication.</p> <p>Discussion</p> <p>The PROVHILO trial is the first randomized controlled trial powered to investigate whether an open lung mechanical ventilation strategy in short-term mechanical ventilation prevents against postoperative pulmonary complications.</p> <p>Trial registration</p> <p>ISRCTN: <a href="http://www.controlled-trials.com/ISRCTN70332574">ISRCTN70332574</a></p

Computed tomographic assessment of lung weights in trauma patients with early posttraumatic lung dysfunction

Introduction: Quantitative computed tomography (qCT)-based assessment of total lung weight (M(lung)) has the potential to differentiate atelectasis from consolidation and could thus provide valuable information for managing trauma patients fulfilling commonly used criteria for acute lung injury (ALI). We hypothesized that qCT would identify atelectasis as a frequent mimic of early posttraumatic ALI. Methods: In this prospective observational study, M(lung) was calculated by qCT in 78 mechanically ventilated trauma patients fulfilling the ALI criteria at admission. A reference interval for M(lung) was derived from 74 trauma patients with morphologically and functionally normal lungs (reference). Results are given as medians with interquartile ranges. Results: The ratio of arterial partial pressure of oxygen to the fraction of inspired oxygen was 560 (506 to 616) mmHg in reference patients and 169 (95 to 240) mmHg in ALI patients. The median reference M(lung) value was 885 (771 to 973) g, and the reference interval for M(lung) was 584 to 1164 g, which matched that of previous reports. Despite the significantly greater median M(lung) value (1088 (862 to 1,342) g) in the ALI group, 46 (59%) ALI patients had M(lung) values within the reference interval and thus most likely had atelectasis. In only 17 patients (22%), Mlung was increased to the range previously reported for ALI patients and compatible with lung consolidation. Statistically significant differences between atelectasis and consolidation patients were found for age, Lung Injury Score, Glasgow Coma Scale score, total lung volume, mass of the nonaerated lung compartment, ventilator-free days and intensive care unit-free days. Conclusions: Atelectasis is a frequent cause of early posttraumatic lung dysfunction. Differentiation between atelectasis and consolidation from other causes of lung damage by using qCT may help to identify patients who could benefit from management strategies such as damage control surgery and lung-protective mechanical ventilation that focus on the prevention of pulmonary complications.Leipzig University Hospita