TY - JOUR
T1 - A Deep Learning Pipeline for Assessing Ventricular Volumes from a Cardiac MRI Registry of Patients with Single Ventricle Physiology
AU - Yao, Tina
AU - St. Clair, Nicole
AU - Miller, Gabriel F.
AU - Dorfman, Adam L.
AU - Fogel, Mark A.
AU - Ghelani, Sunil
AU - Krishnamurthy, Rajesh
AU - Lam, Christopher Z.
AU - Quail, Michael
AU - Robinson, Joshua D.
AU - Schidlow, David
AU - Slesnick, Timothy C.
AU - Weigand, Justin
AU - Steeden, Jennifer A.
AU - Rathod, Rahul H.
AU - Muthurangu, Vivek
N1 - Publisher Copyright:
© RSNA, 2023.
PY - 2024/1
Y1 - 2024/1
N2 - Purpose: To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods: This retrospective study used 250 cardiac MRI examinations (November 2007–December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two U-Net 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) by using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. Results: There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volum(EDV) (bias: −0.6 mL/m2, LOA: −20.6 to 19.5 mL/m2) and end-systolic volume (ESV) (bias: −1.1 mL/m2, LOA: −18.1 to 15.9 mL/ m2), with high intraclass correlation coefficients (ICCs > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agrement for ventricular mass (bias: −1.9 g/m2, LOA: −17.3 to 13.5 g/m2) and an ICC of 0.94. There was also acceptable agreement for stroke volume (bias: 0.6 mL/m2, LOA: −17.2 to 18.3 mL/m2) and ejection fraction (bias: 0.6%, LOA: −12.2% to 13.4%), with high ICCs (>0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. Conclusion: The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multicenters. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry.
AB - Purpose: To develop an end-to-end deep learning (DL) pipeline for automated ventricular segmentation of cardiac MRI data from a multicenter registry of patients with Fontan circulation (Fontan Outcomes Registry Using CMR Examinations [FORCE]). Materials and Methods: This retrospective study used 250 cardiac MRI examinations (November 2007–December 2022) from 13 institutions for training, validation, and testing. The pipeline contained three DL models: a classifier to identify short-axis cine stacks and two U-Net 3+ models for image cropping and segmentation. The automated segmentations were evaluated on the test set (n = 50) by using the Dice score. Volumetric and functional metrics derived from DL and ground truth manual segmentations were compared using Bland-Altman and intraclass correlation analysis. The pipeline was further qualitatively evaluated on 475 unseen examinations. Results: There were acceptable limits of agreement (LOA) and minimal biases between the ground truth and DL end-diastolic volum(EDV) (bias: −0.6 mL/m2, LOA: −20.6 to 19.5 mL/m2) and end-systolic volume (ESV) (bias: −1.1 mL/m2, LOA: −18.1 to 15.9 mL/ m2), with high intraclass correlation coefficients (ICCs > 0.97) and Dice scores (EDV, 0.91 and ESV, 0.86). There was moderate agrement for ventricular mass (bias: −1.9 g/m2, LOA: −17.3 to 13.5 g/m2) and an ICC of 0.94. There was also acceptable agreement for stroke volume (bias: 0.6 mL/m2, LOA: −17.2 to 18.3 mL/m2) and ejection fraction (bias: 0.6%, LOA: −12.2% to 13.4%), with high ICCs (>0.81). The pipeline achieved satisfactory segmentation in 68% of the 475 unseen examinations, while 26% needed minor adjustments, 5% needed major adjustments, and in 0.4%, the cropping model failed. Conclusion: The DL pipeline can provide fast standardized segmentation for patients with single ventricle physiology across multicenters. This pipeline can be applied to all cardiac MRI examinations in the FORCE registry.
KW - Adults and Pediatrics
KW - Cardiac
KW - Congenital
KW - MR Imaging
KW - Quantification
KW - Segmentation
KW - Volume Analysis
UR - http://www.scopus.com/inward/record.url?scp=85184910196&partnerID=8YFLogxK
U2 - 10.1148/ryai.230132
DO - 10.1148/ryai.230132
M3 - Article
AN - SCOPUS:85184910196
SN - 2638-6100
VL - 6
JO - Radiology: Artificial Intelligence
JF - Radiology: Artificial Intelligence
IS - 1
M1 - e230132
ER -