Why the study?
Functional parameter quantification of the left ventricle still requires manual involvement, which is a time-consuming and less reproducible task.
Does the JANet model improve the accuracy of left ventricular segmentation in echocardiograms compared to state-of-the-art models?
Does the JANet model improve the accuracy of left ventricular segmentation in echocardiograms compared to state-of-the-art models?
The JANet deep learning model provides highly accurate, automated left ventricular segmentation in echocardiograms, performing comparably to clinicians.
Answers are grounded in this paper and cite their sources.
JANet enables automated LV segmentation on echocardiography; leaves open prospective validation before clinical adoption.
Multiple cardiac diseases are closely associated with functional parameters of the left ventricle, but functional parameter quantification still requires manual involvement, a time-consuming and less reproducible task. We develop a joint attention network (JANet) and expand it into two versions (V1 and V2) that can be used to segment the left ventricular region in echocardiograms to assist physicians in diagnosis. V1 is a smaller model with a size of 56.3 MB, and V2 has a higher accuracy. The proposed JANet V1 and V2 achieve a mean dice score (DSC) of 93.59/93.69(V1/V2), respectively, outperforming the state-of-the-art models. We grade 1264 patients with 87.24/87.50 (V1/V2) accuracy when using the 2-level classification criteria and 83.62/84.18 (V1/V2) when using the 5-level classification criteria. The results of the consistency analysis show that the proposed method is comparable to that of clinicians.
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Su et al. (2023) studied this question.
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