Cardiac contour delineation currently presents several proposals for automatic segmentation in short-axis MRI. However, most of them have drawbacks such as low accuracy with images from other MRI scanners, lack of description of the algorithms, and difficult access for clinical or research use. For this reason, we present VENTSEG, a deep learning framework for cardiac segmentation implemented in Matlab and Python. Our dice coefficient results segmenting images from the CNN validation set are 85.55% and 96.17% on anonymized clinical data. These results demonstrate the generalization of the framework on multidomain images.
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