MultiJSQ: Direct joint segmentation and quantification of left ventricle with deep multitask-derived regression network

Abstract

Quantitative analysis of clinical function parameters from MRI images is crucial for diagnosing and assessing cardiovascular disease. However, the manual calculation of these parameters is challenging due to the high variability among patients and the time-consuming nature of the process. In this study, the authors introduce a framework named MultiJSQ, comprising the feature presentation network (FRN) and the indicator prediction network (IEN), which is designed for simultaneous joint segmentation and quantification. The FRN is tailored for representing global image features, facilitating the direct acquisition of left ventricle (LV) contour images through pixel classification. Additionally, the IEN incorporates specifically designed modules to extract relevant clinical indices. The authors’ method considers the interdependence of different tasks, demonstrating the validity of these relationships and yielding favourable results. Through extensive experiments on cardiac MR images from 145 patients, MultiJSQ achieves impressive outcomes, with low mean absolute errors of 124 mm², 1.72 mm, and 1.21 mm for areas, dimensions, and regional wall thicknesses, respectively, along with a Dice metric score of 0.908. The experimental findings underscore the excellent performance of our framework in LV segmentation and quantification, highlighting its promising clinical application prospects.