Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients.
For this reason, we propose CRISP a ContRastive Image Segmentation for uncertainty Prediction method.
Causally-enabled machine learning frameworks could help clinicians to identify the best course of treatments by answering counterfactual questions.
Analysis of cardiac ultrasound images is commonly performed in routine clinical practice for quantification of cardiac function.
We achieve an average frame distance of 3. 36 frames for the ES and 7. 17 frames for the ED on videos of arbitrary length.