CorSeg-CineSAX: An Open-Source Deep Learning Framework for Fully Automatic Segmentation of Short-Axis Cine Cardiac MRI Across Multiple Cardiac Diseases

Background: Segmentation of the left ventricular (LV) myocardium, LV cavity, and right ventricular (RV) cavity on short-axis cine cardiac magnetic resonance (CMR) images is essential for deriving clinical functional parameters, but existing automated tools rarely combine full public availability (source code, trained weights, and a deployment tool) with validation across diverse centers, vendors, and disease categories, particularly at the technically demanding basal right/left ventricular outflow tract (RVOT/LVOT) level. Methods: We trained a two-dimensional, slice-by-slice MedNeXt-L model, without region-of-interest cropping, on a private cohort of 2,382 subjects (5 disease categories) from 12 domestic centers, and evaluated it on an independent internal test set (n = 650) and three independent external public datasets (ACDC, M and Ms1, M and Ms2; n = 855 combined), spanning 16 disease categories in total (11 never seen during training). Segmentation accuracy (Dice similarity coefficient [DSC], 95th-percentile Hausdorff distance) was assessed overall, by anatomical slice position (including the basal RVOT/LVOT level), and by disease category, center, vendor, and field strength, alongside agreement between automated and manual clinical functional parameters and benchmarking against human inter-observer variability (three independent observers, 50-subject subsample). Results: Mean DSC was 0.908 on the internal test set and 0.883 on the external test set (combined, 0.893; n = 1,505, 31,440 slices). The basal RVOT/LVOT level achieved a mean DSC of 0.904, comparable to the basal (0.910) and mid-ventricular (0.895) levels; the apex was the most challenging position (mean DSC 0.842). Performance on the 11 disease categories absent from training (mean DSC range, 0.874-0.904) was comparable to that on the five categories represented during training (0.881-0.936). Automated LV cavity and right ventricular segmentation matched or exceeded human inter-observer agreement (DSC 0.923 vs. 0.894 and 0.910 vs. 0.906, respectively), while LV myocardium remained somewhat below it (0.847 vs. 0.875). Agreement with manual functional-parameter measurements was strong for LV/RV volumes and LV mass (intraclass correlation coefficient [ICC] >= 0.961) and weaker for LV ejection fraction (ICC 0.855) and RV ejection fraction (ICC 0.900), both below the corresponding human inter-observer benchmarks (0.969 and 0.931). Conclusions: CorSeg-CineSAX provides an openly released (code, trained weights, and a standalone GUI application), transparently validated framework for fully automatic CMR short-axis segmentation, with performance at the basal outflow-tract level comparable to other anatomical positions and segmentation accuracy for the LV cavity and right ventricle approaching human inter-observer variability.