An Overview of Deep Learning Methods for Left Ventricle Segmentation

Abstract

Cardiac health diseases are one of the key causes of death around the globe. The number of heart patients has considerably increased during the pandemic. Therefore, it is crucial to assess and analyze the medical and cardiac images. Deep learning architectures, specifically convolutional neural networks have profoundly become the primary choice for the assessment of cardiac medical images. The left ventricle is a vital part of the cardiovascular system where the boundary and size perform a significant role in the evaluation of cardiac function. Due to automatic segmentation and good promising results, the left ventricle segmentation using deep learning has attracted a lot of attention. This article presents a critical review of deep learning methods used for the left ventricle segmentation from frequently used imaging modalities including magnetic resonance images, ultrasound, and computer tomography. This study also demonstrates the details of the network architecture, software, and hardware used for training along with publicly available cardiac image datasets and self-prepared dataset details incorporated. The summary of the evaluation matrices with results used by different researchers is also presented in this study. Finally, all this information is summarized and comprehended in order to assist the readers to understand the motivation and methodology of various deep learning models, as well as exploring potential solutions to future challenges in LV segmentation.

Meta-Learning Based Classification Model for Cardiovascular Disease

“…An Overview of Deep Learning Methods for Left Ventricle Segmentation was given in [17]. Nahian Ibn et al [18] proposed a deep learning approach to cardiovascular disease classification employing modified ECG signal from empirical mode decomposition.…”

Assessment of deep learning segmentation for real-time free-breathing cardiac magnetic resonance imaging at rest and under exercise stress

“…In research, a variety of deep learning methods have been developed for segmentation in CMR 17 . However, nearly all of these methods are focused on conventional breathold cine imaging 18 . In real-time MRI, automatic segmentation becomes more important, because a series of heart beats is acquired instead of a single cine loop.…”

Deep learning‐based intraoperative video analysis for supporting surgery

“…Deep learning mimics the pattern of the human nervous system to simulate human learning and cognitive processes, and can learn simple features automatically and convert them to more complex features to resolve problems. [5][6][7] The deep learning has the main advantages: high precision, deep learning can learn features from a large amount of data and achieve high-precision prediction and classification by continuously optimizing models. Strong adaptability, deep learning can achieve strong adaptability to different scenarios through continuous learning and parameter adjustment, and can handle complex data structures and nonlinear problems.…”

“…Simple feature algorithms hardly meet the requirements of accuracy and robustness. Deep learning mimics the pattern of the human nervous system to simulate human learning and cognitive processes, and can learn simple features automatically and convert them to more complex features to resolve problems 5‐7 . The deep learning has the main advantages: high precision, deep learning can learn features from a large amount of data and achieve high‐precision prediction and classification by continuously optimizing models.…”