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

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

Image-guided ToF depth upsampling: a survey

Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies

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