Do Rib-Based Anchors Impair Chest Wall Motion in Early Onset Scoliosis (EOS)?

Purpose: There is a concern in pediatric surgery practice that rib-based fixation may limit chest wall motion in early onset scoliosis (EOS). The purpose of this study is to address the above concern by assessing the contribution of chest wall excursion to respiration before and after surgery. Methods: Quantitative dynamic magnetic resonance imaging (QdMRI) is performed on EOS patients (before and after surgery) and normal children in this retrospective study. QdMRI is purely an image-based approach and allows free breathing image acquisition. Tidal volume parameters for chest walls (CWtv) and hemi-diaphragms (Dtv) were analyzed on concave and convex sides of the spinal curve. EOS patients (1-14 years) and normal children (5-18 years) were enrolled, with an average interval of two years for dMRI acquisition before and after surgery. Results: CWtv significantly increased after surgery in the global comparison including all EOS patients (p < 0.05). For main thoracic curve (MTC) EOS patients, CWtv significantly improved by 50.24% (concave side) and 35.17% (convex side) after age correction (p < 0.05) after surgery. The average ratio of Dtv to CWtv on the convex side in MTC EOS patients was not significantly different from that in normal children (p=0.78), although the concave side showed the difference to be significant. Conclusion: Chest wall component tidal volumes in EOS patients measured via QdMRI did not decrease after rib-based surgery, suggesting that rib-based fixation does not impair chest wall motion in pediatric patients with EOS.

Virtual Growing Child (VGC): A general normative comparative system via quantitative dynamic MRI for quantifying pediatric regional respiratory anomalies with application in thoracic insufficiency syndrome (TIS).

Background: A normative database of regional respiratory structure and function in healthy children does not exist. Methods: VGC provides a database with four categories of regional respiratory measurement parameters including morphological, architectural, dynamic, and developmental. The database has 3,820 3D segmentations (around 100,000 2D slices with segmentations). Age and gender group analysis and comparisons for healthy children were performed using those parameters via two-sided t-testing to compare mean measurements, for left and right sides at end-inspiration (EI) and end-expiration (EE), for different age and gender specific groups. We also apply VGC measurements for comparison with TIS patients via an extrapolation approach to estimate the association between measurement and age via a linear model and to predict measurements for TIS patients. Furthermore, we check the Mahalanobis distance between TIS patients and healthy children of corresponding age. Findings: The difference between male and female groups (10-12 years) behave differently from that in other age groups which is consistent with physiology/natural growth behavior related to adolescence with higher right lung and right diaphragm tidal volumes for females(p<0.05). The comparison of TIS patients before and after surgery show that the right and left components are not symmetrical, and the left side diaphragm height and tidal volume has been significantly improved after surgery (p <0.05). The left lung volume at EE, and left diaphragm height at EI of TIS patients after surgery are closer to the normal children with a significant smaller Mahalanobis distance (MD) after surgery (p<0.05). Interpretation: The VGC system can serve as a reference standard to quantify regional respiratory abnormalities on dMRI in young patients with various respiratory conditions and facilitate treatment planning and response assessment. Funding: The grant R01HL150147 from the National Institutes of Health (PI Udupa).

Interactive Segmentation of Lung Tissue and Lung Excursion in Thoracic Dynamic MRI Based on Shape-guided Convolutional Neural Networks.

Purpose: Lung tissue and lung excursion segmentation in thoracic dynamic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders such as Thoracic Insufficiency Syndrome (TIS). However, the complex variability of intensity and shape of anatomical structures and the low contrast between the lung and surrounding tissue in MR images seriously hamper the accuracy and robustness of automatic segmentation methods. In this paper, we develop an interactive deep-learning based segmentation system to solve this problem. Material & Methods: Considering the significant difference in lung morphological characteristics between normal subjects and TIS subjects, we utilized two independent data sets of normal subjects and TIS subjects to train and test our model. 202 dMRI scans from 101 normal pediatric subjects and 92 dMRI scans from 46 TIS pediatric subjects were acquired for this study and were randomly divided into training, validation, and test sets by an approximate ratio of 5:1:4. First, we designed an interactive region of interest (ROI) strategy to detect the lung ROI in dMRI for accelerating the training speed and reducing the negative influence of tissue located far away from the lung on lung segmentation. Second, we utilized a modified 2D U-Net to segment the lung tissue in lung ROIs, in which the adjacent slices are utilized as the input data to take advantage of the spatial information of the lungs. Third, we extracted the lung shell from the lung segmentation results as the shape feature and inputted the lung ROIs with shape feature into another modified 2D U-Net to segment the lung excursion in dMRI. To evaluate the performance of our approach, we computed the Dice coefficient (DC) and max-mean Hausdorff distance (MM-HD) between manual and automatic segmentations. In addition, we utilized Coefficient of Variation (CV) to assess the variability of our method on repeated dMRI scans and the differences of lung tidal volumes computed from the manual and automatic segmentation results. Results: The proposed system yielded mean Dice coefficients of 0.96±0.02 and 0.89±0.05 for lung segmentation in dMRI of normal subjects and TIS subjects, respectively, demonstrating excellent agreement with manual delineation results. The Coefficient of Variation and p-values show that the estimated lung tidal volumes of our approach are statistically indistinguishable from those derived by manual segmentations. Conclusions: The proposed approach can be applied to lung tissue and lung excursion segmentation from dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be utilized in the assessment of patients with TIS via dMRI routinely.

An Anthropomorphic Diagnosis System of Pulmonary Nodules using Weak Annotation-Based Deep Learning.

Purpose: To develop an anthropomorphic diagnosis system of pulmonary nodules (PN) based on Deep learning (DL) that is trained by weak annotation data and has comparable performance to full-annotation based diagnosis systems. Methods: The proposed system uses deep learning (DL) models to classify PNs (benign vs. malignant) with weak annotations, which eliminates the need for time-consuming and labor-intensive manual annotations of PNs. Moreover, the PN classification networks, augmented with handcrafted shape features acquired through the ball-scale transform technique, demonstrate capability to differentiate PNs with diverse labels, including pure ground-glass opacities, part-solid nodules, and solid nodules. Results: The experiments were conducted on two lung CT datasets: (1) public LIDC-IDRI dataset with 1,018 subjects, (2) In-house dataset with 2740 subjects. Through 5-fold cross-validation on two datasets, the system achieved the following results: (1) an Area Under Curve (AUC) of 0.938 for PN localization and an AUC of 0.912 for PN differential diagnosis on the LIDC-IDRI dataset of 814 testing cases, (2) an AUC of 0.943 for PN localization and an AUC of 0.815 for PN differential diagnosis on the in-house dataset of 822 testing cases. These results demonstrate comparable performance to full annotation-based diagnosis systems. Conclusions: Our system can efficiently localize and differentially diagnose PNs even in resource-limited environments with good robustness across different grade and morphology sub-groups in the presence of deviations due to the size, shape, and texture of the nodule, indicating its potential for future clinical translation. Summary: An anthropomorphic diagnosis system of pulmonary nodules (PN) based on deep learning and weak annotation was found to achieve comparable performance to full-annotation dataset-based diagnosis systems, significantly reducing the time and the cost associated with the annotation. Key Points: A fully automatic system for the diagnosis of PN in CT scans using a suitable deep learning model and weak annotations was developed to achieve comparable performance (AUC = 0.938 for PN localization, AUC = 0.912 for PN differential diagnosis) with the full-annotation based deep learning models, reducing around 30%∼80% of annotation time for the experts.The integration of the hand-crafted feature acquired from human experts (natural intelligence) into the deep learning networks and the fusion of the classification results of multi-scale networks can efficiently improve the PN classification performance across different diameters and sub-groups of the nodule.

Assessment of Regional Functional Effects of Surgical Treatment in Thoracic Insufficiency Syndrome via Dynamic Magnetic Resonance Imaging.

BACKGROUND: Quantitative regional assessment of thoracic function would enable clinicians to better understand the regional effects of therapy and the degree of deviation from normality in patients with thoracic insufficiency syndrome (TIS). The purpose of this study was to determine the regional functional effects of surgical treatment in TIS via quantitative dynamic magnetic resonance imaging (MRI) in comparison with healthy children. METHODS: Volumetric parameters were derived via 129 dynamic MRI scans from 51 normal children (November 2017 to March 2019) and 39 patients with TIS (preoperatively and postoperatively, July 2009 to May 2018) for the left and right lungs, the left and right hemi-diaphragms, and the left and right hemi-chest walls during tidal breathing. Paired t testing was performed to compare the parameters from patients with TIS preoperatively and postoperatively. Mahalanobis distances between parameters of patients with TIS and age-matched normal children were assessed to evaluate the closeness of patient lung function to normality. Linear regression functions were utilized to estimate volume deviations of patients with TIS from normality, taking into account the growth of the subjects. RESULTS: The mean Mahalanobis distances for the right hemi-diaphragm tidal volume (RDtv) were -1.32 ± 1.04 preoperatively and -0.05 ± 1.11 postoperatively (p = 0.001). Similarly, the mean Mahalanobis distances for the right lung tidal volume (RLtv) were -1.12 ± 1.04 preoperatively and -0.10 ± 1.26 postoperatively (p = 0.01). The mean Mahalanobis distances for the ratio of bilateral hemi-diaphragm tidal volume to bilateral lung tidal volume (BDtv/BLtv) were -1.68 ± 1.21 preoperatively and -0.04 ± 1.10 postoperatively (p = 0.003). Mahalanobis distances decreased after treatment, suggesting reduced deviations from normality. Regression results showed that all volumes and tidal volumes significantly increased after treatment (p < 0.001), and the tidal volume increases were significantly greater than those expected from normal growth for RDtv, RLtv, BDtv, and BLtv (p < 0.05). CONCLUSIONS: Postoperative tidal volumes of bilateral lungs and bilateral hemi-diaphragms of patients with TIS came closer to those of normal children, indicating positive treatment effects from the surgical procedure. Quantitative dynamic MRI facilitates the assessment of regional effects of a surgical procedure to treat TIS. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

QdMRI: A system for comprehensive analysis of thoracic dynamics via dynamic MRI.

Quantitative thoracic dynamic magnetic resonance imaging (QdMRI), a recently developed technique, provides a potential solution for evaluating treatment effects in thoracic insufficiency syndrome (TIS). In this paper, we integrate all related algorithms and modules during our work from the past 10 years on TIS into one system, named QdMRI, to address the following questions: (1) How to effectively acquire dynamic images? For many TIS patients, subjects are unable to cooperate with breathing instructions during image acquisition. Image acquisition can only be implemented under free-breathing conditions, and it is not feasible to use a surrogate device for tracing breathing signals. (2) How to assess the thoracic structures from the acquired image, such as lungs, left and right, separately? (3) How to depict the dynamics of thoracic structures due to respiration motion? (4) How to use the structural and functional information for the quantitative evaluation of surgical TIS treatment and for the design of the surgery plan? The QdMRI system includes 4 major modules: dynamic MRI (dMRI) acquisition, 4D image construction, image segmentation (from 4D image), and visualization of segmentation results, dynamic measurements, and comparisons of measurements from TIS patients with those from normal children. Scanning/image acquisition time for one subject is ~20 minutes, 4D image construction time is ~5 minutes, image segmentation of lungs via deep learning is 70 seconds for all time points (with the average DICE 0.96 in healthy children), and measurement computation time is 2 seconds.

Automatic lung segmentation in dynamic thoracic MRI using two-stage deep convolutional neural networks.

Lung segmentation in dynamic thoracic magnetic resonance imaging (dMRI) is a critical step for quantitative analysis of thoracic structure and function in patients with respiratory disorders. Some semi-automatic and automatic lung segmentation methods based on traditional image processing models have been proposed mainly for CT with good performance. However, the low efficiency and robustness of these methods and inapplicability to dMRI make them unsuitable to segment the large numbers of dMRI datasets. In this paper, we present a novel automatic lung segmentation approach for dMRI based on two-stage convolutional neural networks (CNNs). In the first stage, we utilize the modified min-max normalization method to pre-process MRI for increasing the contrast between the lung and surrounding tissue and propose a corner-points and CNN based region of interest (ROI) detection strategy to extract the lung ROI from sagittal dMRI slices, which can reduce the negative influence of tissues located far away from the lung. In the second stage, we input the adjacent ROIs of target slices into the modified 2D U-Net to segment the lung tissue. The qualitative and quantitative results demonstrate that our approach achieves high accuracy and stability in terms of lung segmentation for dMRI.

Automatic upper airway segmentation in static and dynamic MRI via anatomy-guided convolutional neural networks.

PURPOSE: Upper airway segmentation on MR images is a prerequisite step for quantitatively studying the anatomical structure and function of the upper airway and surrounding tissues. However, the complex variability of intensity and shape of anatomical structures and different modes of image acquisition commonly used in this application makes automatic upper airway segmentation challenging. In this paper, we develop and test a comprehensive deep learning-based segmentation system for use on MR images to address this problem. MATERIALS AND METHODS: In our study, both static and dynamic MRI data sets are utilized, including 58 axial static 3D MRI studies, 22 mid-retropalatal dynamic 2D MRI studies, 21 mid-retroglossal dynamic 2D MRI studies, 36 mid-sagittal dynamic 2D MRI studies, and 23 isotropic dynamic 3D MRI studies, involving a total of 160 subjects and over 20 000 MRI slices. Samples of static and 2D dynamic MRI data sets were randomly divided into training, validation, and test sets by an approximate ratio of 5:2:3. Considering that the variability of annotation data among 3D dynamic MRIs was greater than for other MRI data sets, we increased the ratio of training data for these data to improve the robustness of the model. We designed a unified framework consisting of the following procedures. For static MRI, a generalized region-of-interest (GROI) strategy is applied to localize the partitions of nasal cavity and other portions of upper airway in axial data sets as two separate subobjects. Subsequently, the two subobjects are segmented by two separate 2D U-Nets. The two segmentation results are combined as the whole upper airway structure. The GROI strategy is also applied to other MRI modes. To minimize false-positive and false-negative rates in the segmentation results, we employed a novel loss function based explicitly on these rates to train the segmentation networks. An inter-reader study is conducted to test the performance of our system in comparison to human variability in ground truth (GT) segmentation of these challenging structures. RESULTS: The proposed approach yielded mean Dice coefficients of 0.84±0.03, 0.89±0.13, 0.84±0.07, and 0.86±0.05 for static 3D MRI, mid-retropalatal/mid-retroglossal 2D dynamic MRI, mid-sagittal 2D dynamic MRI, and isotropic dynamic 3D MRI, respectively. The quantitative results show excellent agreement with manual delineation results. The inter-reader study results demonstrate that the segmentation performance of our approach is statistically indistinguishable from manual segmentations considering the inter-reader variability in GT. CONCLUSIONS: The proposed method can be utilized for routine upper airway segmentation from static and dynamic MR images with high accuracy and efficiency. The proposed approach has the potential to be employed in other dynamic MRI-related applications, such as lung or heart segmentation.

Automatic left ventricle segmentation in short-axis MRI using deep convolutional neural networks and central-line guided level set approach.

In the clinical diagnosis of cardiovascular diseases, left ventricle (LV) segmentation in cardiac magnetic resonance images (MRI) is an indispensable procedure for doctors. To reduce the time needed for diagnosis, we develop an automatic LV segmentation method by integrating the convolutional neural network (CNN) with the level set approach. Firstly, a CNN based myocardial central-line detection algorithm was proposed to replace the manual initialization process for traditional level set approaches. Secondly, we present a novel central-line guided level set approach (CGLS) for delineating the myocardium region. In particular, we incorporate the myocardial central-line into the level set energy formulation as a constraint term. It plays two important roles in the iterative process: restricting the zero-level contour to stay around the myocardial central-line and preserving the anatomical geometry of myocardium segmentation result. In experiments, our method yields results as below: (1) 1.74 mm and 2.06 mm in terms of epicardium and endocardium perpendicular distance on MICCAI 2009 dataset, (2) 0.955 and 0.853 in terms of LV and myocardium Dice metric at the end-diastole on ACDC MICCAI 2017 dataset. The experimental data demonstrate that our method outperforms some state-of-the-art methods and achieves a good agreement with the manual segmentation results.

Dynamic pixel-wise weighting-based fully convolutional neural networks for left ventricle segmentation in short-axis MRI.

Left ventricle (LV) segmentation in cardiac MRI is an essential procedure for quantitative diagnosis of various cardiovascular diseases. In this paper, we present a novel fully automatic left ventricle segmentation approach based on convolutional neural networks. The proposed network fully takes advantages of the hierarchical architecture and integrate the multi-scale feature together for segmenting the myocardial region of LV. Moreover, we put forward a dynamic pixel-wise weighting strategy, which can dynamically adjust the weight of each pixel according to the segmentation accuracy of upper layer and force the pixel classifier to take more attention on the misclassified ones. By this way, the LV segmentation performance of our method can be improved a lot especially for the apical and basal slices in cine MR images. The experiments on the CAP database demonstrate that our method achieves a substantial improvement compared with other well-know deep learning methods. Beside these, we discussed two major limitations in convolutional neural networks-based semantic segmentation methods for LV segmentation.

Experimental study of the inhibition effect of CXCL12/CXCR4 in malignant pleural mesothelioma.

Previous studies have demonstrated that CXCL12/CXCR4 axis is closely related to tumors such as malignant pleural mesothelioma (MPM). This research was conducted in order to detect whether CXCL12/CXCR4 inhibitors could restrain MPM and have a synergistic effect with chemotherapy, also to investigate the relationship of CXCL12/CXCR4 with other gene expressions in MPM. Forty mice were injected MPM cells and randomly divided into four groups: the PBS (control group), AMD3100 (CXCR4-CXCL12 antagonist), pemetrexed and AMD3100 plus pemetrexed. The mice were treated respectively for duration of 3 weeks. The size, bioluminescence and weight of tumors were measured. The differences between gene expressions in each group were analyzed. The tumor weights of each treatment group were lower than that of the control group (p<0.05). The bioluminescence of the tumor of the AMD3100 treatment group and the AMD3100 plus pemetrexed treatment group were lower than that of the control group (p<0.05), and AMD3100 was shown to have synergistic effects with pemetrexed (p<0.05). Among the 2.5 billion genes, several hundreds of genes expressed differently between groups. Results show that AMD3100 and pemetrexed can inhibit the growth of MPM in vivo, also that there is a better result if both are used together. Our findings suggest that CXCL12/CXCR4 axis affects a certain amount of gene expression in MPM.