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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