Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance

By Tuan Anh Ngo, Zhi Lu, Gustavo Carneiro. Medical Image Anal., 2017

310 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/c60d351775053356dba871402fac2b180a5a18b3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The present and future of deep learning in radiology. (2019, methodology): “Although DBN is used for unsupervised learning it has been implemented for segmentation [52].”
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (2017, methodology): “In this paper, experiments have been carried out on the publicly available brain MR image database – LPBA40 [11], which has 40 volumes with 56 structures delineated.”
Artificial Intelligence in Cardiovascular Imaging (2019, methodology): “More recently, DL methods combined with level sets have been applied for the segmentation of LV from cardiac magnetic resonance (CMR) images (44).”
Preparing Medical Imaging Data for Machine Learning (2020, methodology): “For example, a head CT might have findings supporting a diagnosis of stroke, but an MRI could definitively confirm.”
Deep learning in medical imaging and radiation therapy. (2018, methodology): “Multiple works subsequently refine these likelihood maps using classic segmentation methods, such as level sets, 76 – 79 , graph cuts, 80 and model-based methods, 81,82 to achieve a more accurate segmentation than using the likelihood maps alone.”
Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018, methodology): “[31] used a deep belief network (DBN) to accurately initialize and”
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II (2017, unknown): “To solve this problem, an iterative reweighted least square process [9], which poses the optimization as a sequence of least square problems while the weight of each derivative is updated based on the previous iteration, is implemented.”
Deep Learning for Cardiac Image Segmentation: A Review (2019, background): “, level-sets (98, 181), deformable models (47, 96, 182), atlas-based methods (97, 111), and graph-cut based methods (183).”
Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation (2017, methodology): “We are interested in exploring using level-sets to refine the segmentation as in [9] and correcting the segmentation with minimal manual intervention.”
Deep Learning and Its Applications in Biomedicine (2018, methodology): “…medical applications employing a deep learning architecture have been demonstrated in segmenting the left ventricle of the heart from the MR data [112], the pancreas through computed tomography (CT) [113], tibial cartilage through magnetic resonance imaging (MRI) [114], the prostate through MRI…”