Neural scaling laws fitted to subset performance on CAMUS and CEUS echocardiography datasets enable selection of smaller networks achieving state-of-the-art myocardial segmentation with 240-fold parameter reduction and clinical equivalence to expert cardiologists in perfusion quantification.
Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Myocardial Contrast Echocardiography (MCE) with micro-bubble contrast agent enables myocardial perfusion quantification which is invaluable for the early detection of coronary artery diseases. In this paper, we proposed a new segmentation method called Shape Model guided Random Forests (SMRF) for the analysis of MCE data. The proposed method utilizes a statistical shape model of the myocardium to guide the Random Forest (RF) segmentation in two ways. First, we introduce a novel Shape Model (SM) feature which captures the global structure and shape of the myocardium to produce a more accurate RF probability map. Second, the shape model is fitted to the RF probability map to further refine and constrain the final segmentation to plausible myocardial shapes. Evaluated on clinical MCE images from 15 patients, our method obtained promising results (Dice=0.81, Jaccard=0.70, MAD=1.68 mm, HD=6.53 mm) and showed a notable improvement in segmentation accuracy over the classic RF and its variants.
fields
eess.IV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Compute-Optimal Network Design for Echocardiography Myocardial Segmentation and Perfusion Quantification using Neural Scaling Laws
Neural scaling laws fitted to subset performance on CAMUS and CEUS echocardiography datasets enable selection of smaller networks achieving state-of-the-art myocardial segmentation with 240-fold parameter reduction and clinical equivalence to expert cardiologists in perfusion quantification.