Automatic segmentation and volumetric assessment of internal organs and fatty tissue: what are the benefits?

Comparison and precision of visceral adipose tissue measurement techniques in a multisite longitudinal study using MRI

BACKGROUND

Measurement of visceral adipose tissue (VAT) using magnetic resonance imaging (MRI) is considered accurate and safe. Single slice measurements perform similar to volumetric measurements for cross-sectional observation studies but may not perform as well for longitudinal studies. This study compared the performance of single slice to volumetric VAT measurements in a prospective longitudinal study. Consistency of results across sites and over time was also evaluated.

METHODS

A total of 935 healthy participants were recruited and scanned with MRI twice, approximately six months apart as part of a randomized, controlled, parallel arm, unblinded study conducted at four clinical centers in the United States. A 3D Dixon MRI sequence was used to image the abdomen, and visceral fat volumes were quantified for the abdomen, reduced coverage volumes (11 and 25 slices), and at single slices positioned at anatomical landmarks. A traveling phantom was scanned twice at all imaging sites.

RESULTS

The correlation of single slice VAT measurement to full abdomen volumetric measurements ranged from 0.78 to 0.93 for cross-sectional observation measurements and 0.30 to 0.55 for longitudinal change. Reduced coverage volumetric measurement outperformed single slice measurements but still showed improved precision with more slices with cross-sectional observation and longitudinal correlations of 0.94 and 0.66 for 11 slices and 0.94 and 0.70 for 25 slices, respectively. No significant differences were observed across sites or over time with the traveling phantom and the volume measurements had a standard deviation of 14.1 mL, 2.6% of the measured volume.

CONCLUSION

Single slice VAT measurements had significantly lower correlation with abdomen VAT volume for longitudinal change than for cross-sectional observation measurements and may not be suitable for longitudinal studies. Data from multiple sites, different scanners, and over time did not show significant differences.

Automated abdominal adipose tissue segmentation and volume quantification on longitudinal MRI using 3D convolutional neural networks with multi-contrast inputs

OBJECTIVE

Increased subcutaneous and visceral adipose tissue (SAT/VAT) volume is associated with risk for cardiometabolic diseases. This work aimed to develop and evaluate automated abdominal SAT/VAT segmentation on longitudinal MRI in adults with overweight/obesity using attention-based competitive dense (ACD) 3D U-Net and 3D nnU-Net with full field-of-view volumetric multi-contrast inputs.

MATERIALS AND METHODS

920 adults with overweight/obesity were scanned twice at multiple 3 T MRI scanners and institutions. The first scan was divided into training/validation/testing sets (n = 646/92/182). The second scan from the subjects in the testing set was used to evaluate the generalizability for longitudinal analysis. Segmentation performance was assessed by measuring Dice scores (DICE-SAT, DICE-VAT), false negatives (FN), and false positives (FP). Volume agreement was assessed using the intraclass correlation coefficient (ICC).

RESULTS

ACD 3D U-Net achieved rapid (< 4.8 s/subject) segmentation with high DICE-SAT (median ≥ 0.994) and DICE-VAT (median ≥ 0.976), small FN (median ≤ 0.7%), and FP (median ≤ 1.1%). 3D nnU-Net yielded rapid (< 2.5 s/subject) segmentation with similar DICE-SAT (median ≥ 0.992), DICE-VAT (median ≥ 0.979), FN (median ≤ 1.1%) and FP (median ≤ 1.2%). Both models yielded excellent agreement in SAT/VAT volume versus reference measurements (ICC > 0.997) in longitudinal analysis.

DISCUSSION

ACD 3D U-Net and 3D nnU-Net can be automated tools to quantify abdominal SAT/VAT volume rapidly, accurately, and longitudinally in adults with overweight/obesity.

Utilizing fully-automated 3D organ segmentation for hepatic steatosis assessment with CT attenuation-based parameters

OBJECTIVES

To investigate the clinical utility of fully-automated 3D organ segmentation in assessing hepatic steatosis on pre-contrast and post-contrast CT images using magnetic resonance spectroscopy (MRS)-proton density fat fraction (PDFF) as reference standard.

MATERIALS AND METHODS

This retrospective study analyzed 362 adult potential living liver donors with abdominal CT scans and MRS-PDFF. Using a deep learning-based tool, mean volumetric CT attenuation of the liver and spleen were measured on pre-contrast (liver(L)_pre and spleen(S)_pre) and post-contrast (L_post and S_post) images. Agreements between volumetric and manual region-of-interest (ROI)-based measurements were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. Diagnostic performances of volumetric parameters (L_pre, liver-minus-spleen (L-S)_pre, L_post, and L-S_post) were evaluated for detecting MRS-PDFF ≥ 5% and ≥ 10% using receiver operating characteristic (ROC) curve analysis and compared with those of ROI-based parameters.

RESULTS

Among the 362 subjects, 105 and 35 had hepatic steatosis with MRS-PDFF ≥ 5% and ≥ 10%, respectively. Volumetric and ROI-based measurements revealed ICCs of 0.974, 0.825, 0.992, and 0.962, with mean differences of -4.2 HU, -3.4 HU, -1.2 HU, and -7.7 HU for L_pre, S_pre, L_post, and S_post, respectively. Volumetric L_pre, L-S_pre, L_post, and L-S_post yielded areas under the ROC curve of 0.813, 0.813, 0.734, and 0.817 for MRS-PDFF ≥ 5%; and 0.901, 0.915, 0.818, and 0.868 for MRS-PDFF ≥ 10%, comparable with those of ROI-based parameters (0.735-0.818; and 0.816-0.895, Ps = 0.228-0.911).

CONCLUSION

Automated 3D segmentation of the liver and spleen in CT scans can provide volumetric CT attenuation-based parameters to detect and grade hepatic steatosis, applicable to pre-contrast and post-contrast images.

CLINICAL RELEVANCE STATEMENT

Volumetric CT attenuation-based parameters of the liver and spleen, obtained through automated segmentation tools from pre-contrast or post-contrast CT scans, can efficiently detect and grade hepatic steatosis, making them applicable for large population data collection.

KEY POINTS

• Automated organ segmentation enables the extraction of CT attenuation-based parameters for the target organ. • Volumetric liver and spleen CT attenuation-based parameters are highly accurate in hepatic steatosis assessment. • Automated CT measurements from pre- or post-contrast imaging show promise for hepatic steatosis screening in large cohorts.