Auto-segmentation of thoraco-abdominal organs in free breathing pediatric dynamic MRI

Auto-segmentation of hemi-diaphragms in free-breathing dynamic MRI of pediatric subjects with thoracic insufficiency syndrome

Purpose

In respiratory disorders such as thoracic insufficiency syndrome (TIS), the quantitative study of the regional motion of the left hemi-diaphragm (LHD) and right hemi-diaphragm (RHD) can give detailed insights into the distribution and severity of the abnormalities in individual patients. Dynamic magnetic resonance imaging (dMRI) is a preferred imaging modality for capturing dynamic images of respiration since dMRI does not involve ionizing radiation and can be obtained under free-breathing conditions. Using 4D images constructed from dMRI of sagittal locations, diaphragm segmentation is an evident step for the said quantitative analysis of LHD and RHD in these 4D images.

Methods

In this paper, we segment the LHD and RHD in three steps: recognition of diaphragm, delineation of diaphragm, and separation of diaphragm along the mid-sagittal plane into LHD and RHD. The challenges involved in dMRI images are low resolution, motion blur, suboptimal contrast resolution, inconsistent meaning of gray-level intensities for the same object across multiple scans, and low signal-to-noise ratio. We have utilized deep learning (DL) concepts such as Path Aggregation Network and Dual Attention Network for the recognition step, Dense-Net and Residual-Net in an enhanced encoder-decoder architecture for the delineation step, and a combination of GoogleNet and Recurrent Neural Network for the identification of the mid-sagittal plane in the separation step. Due to the challenging images of TIS patients attributed to their highly distorted and variable anatomy of the thorax, in such images we localize the diaphragm using the auto-segmentations of the lungs and the thoraco-abdominal skin.

Results

We achieved an average±SD mean-Hausdorff distance of ∼3±3 mm for the delineation step and a positional error of ∼3±3 mm in recognizing the mid-sagittal plane in 100 3D test images of TIS patients with a different set of ∼430 3D images of TIS patients utilized for building the models for delineation, and separation. We showed that auto-segmentations of the diaphragm are indistinguishable from segmentations by experts, in images of near-normal subjects. In addition, the algorithmic identification of the mid-sagittal plane is indistinguishable from its identification by experts in images of near-normal subjects.

Conclusions

Motivated by applications in surgical planning for disorders such as TIS, we have shown an auto-segmentation set-up for the diaphragm in dMRI images of TIS pediatric subjects. The results are promising, showing that our system can handle the aforesaid challenges. We intend to use the auto-segmentations of the diaphragm to create the initial ground truth (GT) for newly acquired data and then refining them, to expedite the process of creating GT for diaphragm motion analysis, and to test the efficacy of our proposed method to optimize pre-treatment planning and post-operative assessment of patients with TIS and other disorders.

Auto-segmentation of thoraco-abdominal organs in pediatric dynamic MRI

Purpose

Analysis of the abnormal motion of thoraco-abdominal organs in respiratory disorders such as the Thoracic Insufficiency Syndrome (TIS) and scoliosis such as adolescent idiopathic scoliosis (AIS) or early onset scoliosis (EOS) can lead to better surgical plans. We can use healthy subjects to find out the normal architecture and motion of a rib cage and associated organs and attempt to modify the patient's deformed anatomy to match to it. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for capturing dynamic images of healthy pediatric subjects. In this paper, we propose an auto-segmentation set-up for the lungs, kidneys, liver, spleen, and thoraco-abdominal skin in these dMRI images which have their own challenges such as poor contrast, image non-standardness, and similarity in texture amongst gas, bone, and connective tissue at several inter-object interfaces.

Methods

The segmentation set-up has been implemented in two steps: recognition and delineation using two deep neural network (DL) architectures (say DL-R and DL-D) for the recognition step and delineation step, respectively. The encoder-decoder framework in DL-D utilizes features at four different resolution levels to counter the challenges involved in the segmentation. We have evaluated on dMRI sagittal acquisitions of 189 (near-)normal subjects. The spatial resolution in all dMRI acquisitions is 1.46 mm in a sagittal slice and 6.00 mm between sagittal slices. We utilized images of 89 (10) subjects at end inspiration for training (validation). For testing we experimented with three scenarios: utilizing (1) the images of 90 (=189-89-10) different (remaining) subjects at end inspiration for testing, (2) the images of the aforementioned 90 subjects at end expiration for testing, and (3) the images of the aforesaid 99 (=89+10) subjects but at end expiration for testing. In some situations, we can take advantage of already available ground truth (GT) of a subject at a particular respiratory phase to automatically segment the object in the image of the same subject at a different respiratory phase and then refining the segmentation to create the final GT. We anticipate that this process of creating GT would require minimal post hoc correction. In this spirit, we conducted separate experiments where we assume to have the ground truth of the test subjects at end expiration for scenario (1), end inspiration for (2), and end inspiration for (3).

Results

Amongst these three scenarios of testing, for the DL-R, we achieve a best average location error (LE) of about 1 voxel for the lungs, kidneys, and spleen and 1.5 voxels for the liver and the thoraco- abdominal skin. The standard deviation (SD) of LE is about 1 or 2 voxels. For the delineation approach, we achieve an average Dice coefficient (DC) of about 0.92 to 0.94 for the lungs, 0.82 for the kidneys, 0.90 for the liver, 0.81 for the spleen, and 0.93 for the thoraco-abdominal skin. The SD of DC is lower for the lungs, liver, and the thoraco-abdominal skin, and slightly higher for the spleen and kidneys.

Conclusions

Motivated by applications in surgical planning for disorders such as TIS, AIS, and EOS, we have shown an auto-segmentation system for thoraco-abdominal organs in dMRI acquisitions. This proposed setup copes with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data.

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).