Automated right ventricle medical imaging and computation of clinical parameters

A system for automatically processing two dimensional (2D) ultrasound images for computing of at least one clinical parameter of a right ventricle (RV), comprising:

• at least one hardware processor executing a code for:
• selecting one 2D ultrasound image of a plurality of 2D ultrasound images depicting at least a RV of a subject, sequentially captured over at least one cardiac cycle of the subject;
• classifying the RV depicted in the selected one 2D ultrasound image into a predefined shape selected from a plurality of predefined shapes;
• interpolating an inner contour of an endocardial border of the RV for the selected one 2D ultrasound image according to the classified predefined shape of the selected one 2D ultrasound image;
• tracking the interpolated inner contour obtained for the one 2D ultrasound image over the plurality of 2D images over the at least one cardiac cycle;
• computing, a RV area of the RV for each respective 2D ultrasound image of the plurality of 2D ultrasound images, according to the tracked interpolated inner contour;
• identifying a first 2D ultrasound image depicting an end-diastole (ED) state according to a maximal value of the RV area for the plurality of 2D images, and a second 2D US image depicting an end-systole(ES) state according to minimal value of the RV area for the plurality of 2D images;
• and
• computing at least one clinical parameter of the RV according to the identified first 2D ultrasound image depicting the ED state and the second 2D US image depicting the ES state.

A non-transitory computer readable medium for automatically processing two dimensional (2D) ultrasound images for computing of at least one clinical parameter indicative of a right ventricle (RV) comprising program instructions which, when executed by a processor, cause the processor to perform:

• selecting one 2D ultrasound image of a plurality of 2D ultrasound images depicting at least a RV of a subject, sequentially captured over at least one cardiac cycle of the subject;
• classifying the RV depicted in the selected one 2D ultrasound image into a predefined shape selected from a plurality of predefined shapes;
• interpolating an inner contour of an endocardial border of the RV for the selected one 2D ultrasound image according to the classified predefined shape of the selected one 2D ultrasound image;
• tracking the interpolated inner contour obtained for the one 2D ultrasound image over the plurality of 2D images over at least one cardiac cycle;
• computing, a RV area of the RV for each respective 2D ultrasound image of the plurality of 2D ultrasound images, according to the tracked interpolated inner contour;
• identifying a first 2D ultrasound image depicting an end-diastole (ED) state according to a maximal value of the RV area for the plurality of 2D images, and a second 2D US image depicting an end-systole(ES) state according to minimal value of the RV area for the plurality of 2D images;
• and
• computing at least one clinical parameter of the RV according to the identified first 2D ultrasound image depicting the ED state and the second 2D US image depicting the ES state.