The segmentation ceiling: why explicit left-ventricular masks do not improve learned ejection-fraction regression
Accurate estimation of left ventricular ejection fraction (EF) from echocardiography is central to cardiovascular care, and deep learning enables automated EF prediction from echocardiographic video. Because EF is clinically derived from left-ventricular (LV) volumes, a widely held intuition is that explicit LV segmentation should improve prediction. We introduce a quantitative criterion, the segmentation ceiling, that makes this testable: from EF as a normalized difference of end-diastolic and end-systolic volumes, we derive in closed form how per-frame segmentation area error propagates into EF error, and thus the accuracy a mask must reach before it can improve on direct regression. Us...
| Authors | Farshid Farhadi Khouzani, Paul La Plante, Bryar Mustafa Shareef et al. |
|---|---|
| Primary category | eess.IV |
| Published | 17 September 2026 |
| Last updated at source | 7 October 2026 |
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