Direct Segmentation-Based Full Quantification for Left Ventricle via Deep Multi-Task Regression Learning Network

Quantitative analysis of the heart is extremely necessary and significant for detecting and diagnosing heart disease, yet there are still some challenges. In this study, we propose a new end-to-end segmentation-based deep multi-task regression learning model (Indices-JSQ) to make a holonomic quantitative analysis of the left ventricle (LV), which contains a segmentation network (Img2Contour) and multi-task regression network (Contour2Indices). First, Img2Contour, which contains a deep convolutional encoder-decoder module, is designed to obtain the LV contour. Then, the predicted contour is fed as input to Contour2Indices for full quantification. On the whole, we take into account the relationship between different tasks, which can serve as a complementary advantage. Meanwhile, instead of using images directly from the original dataset, we creatively use the segmented contour of the original image to estimate the cardiac indices to achieve better and more accurate results. We make experiments on MR sequences of 145 subjects and gain the experimental results of 157 mm 2, 2. 43 mm, 1. 29 mm, and 0. 87 on areas, dimensions, regional wall thicknesses, and Dice Metric, respectively. It intuitively shows that the proposed method outperforms the other state-of-the-art methods and demonstrates that our method has a great potential in cardiac MR images segmentation, comprehensive clinical assessment, and diagnosis.

Authors

• Xiuquan Du
• Renjun Tang
• Susu Yin
• Yanping Zhang
• Shuo Li Case Western Reserve University

Keywords

• Image segmentation
• Estimation
• Feature extraction
• Decoding
• Task analysis
• Heart
• Convolution
• Deep Learning
• Left Ventricular
• Multi-task Learning
• Multi-task Deep Learning
• Full Quantification
• Multi-task Regression
• Magnetic Resonance Imaging
• Cardiac Imaging
• MRI Sequences
• Cardiac Index
• Multi-task Model
• Complementary Advantages
• Comprehensive Clinical Assessment
• Contour Segmentation
• Deep Neural Network
• Convolutional Layers
• Feature Maps
• Clinical Indicators
• Mean Absolute Error
• Network Performance
• Myocardial Area
• Encoder Module
• Feature Map Information
• Decoder Module
• CNN Model
• Direct Estimates
• Average Mean Absolute Error
• Flow Algorithm
• Index Estimation
• Medical Image Analysis
• Left ventricle
• multi-task regression learning
• Adolescent
• Adult
• Aged
• Aged, 80 and over
• Heart Diseases
• Heart Ventricles
• Humans
• Image Interpretation, Computer-Assisted
• Middle Aged
• Regression Analysis
• Young Adult