Automated cardiac bi-ventricular segmentation and motion analysis in a monocrotaline rat model of pulmonary hypertension
Marili Niglas1, Nicoleta Baxan2, Ali Ashek1, Lin Zhao1, Jinming Duan3, Declan O'Regan4, Timothy JW Dawes1,4, Wenjia Bai5, and Lan Zhao1
1National Heart and Lung Institute, Imperial College London, London, United Kingdom, 2Biological Imaging Centre, Imperial College London, London, United Kingdom, 3School of Computer Science, University of Birmingham, London, United Kingdom, 4MRC London Institute of Medical Sciences, Imperial College London, London, United Kingdom, 5Department of Brain Sciences, Imperial College London, London, United Kingdom
Multiple cardiac AI-based image processing pipelines exist for clinical application, yet the equivalent is lacking for rodents. We utilized a fully convolutional network combined with 3D-atlas registration to auto-segment cine images from pulmonary hypertension (PH) rats and produce 3D contraction maps. The auto-segmentations were equivalent to manual (Dice overall >0.7). The volumetric parameters did not differ between methods, except a minor underestimation for RVESV in PH rat (8.2%). 3D contraction maps indicated moderately increased basal wall motion at early (adaptive) stage followed by a 36% reduction at later (maladaptive) stage of PH. This regional motion remodelling correlates with PAH patients.
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