[Retracted] Deep Neural Networks for Medical Image Segmentation
P Malhotra, S Gupta, D Koundal… - Journal of …, 2022 - Wiley Online Library
Image segmentation is a branch of digital image processing which has numerous
applications in the field of analysis of images, augmented reality, machine vision, and many …
applications in the field of analysis of images, augmented reality, machine vision, and many …
Deep learning for cardiac image segmentation: a review
Deep learning has become the most widely used approach for cardiac image segmentation
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
in recent years. In this paper, we provide a review of over 100 cardiac image segmentation …
Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?
Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …
magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish …
Learning active contour models for medical image segmentation
X Chen, BM Williams… - Proceedings of the …, 2019 - openaccess.thecvf.com
Image segmentation is an important step in medical image processing and has been widely
studied and developed for refinement of clinical analysis and applications. New models …
studied and developed for refinement of clinical analysis and applications. New models …
[HTML][HTML] Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Residual attention u-net for automated multi-class segmentation of covid-19 chest ct images
The novel coronavirus disease 2019 (COVID-19) has been spreading rapidly around the
world and caused significant impact on the public health and economy. However, there is …
world and caused significant impact on the public health and economy. However, there is …
A survey on U-shaped networks in medical image segmentations
The U-shaped network is one of the end-to-end convolutional neural networks (CNNs). In
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …
electron microscope segmentation of ISBI challenge 2012, the concise architecture and …
Combo loss: Handling input and output imbalance in multi-organ segmentation
Simultaneous segmentation of multiple organs from different medical imaging modalities is a
crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery …
crucial task as it can be utilized for computer-aided diagnosis, computer-assisted surgery …
Fully convolutional multi-scale residual DenseNets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers
M Khened, VA Kollerathu, G Krishnamurthi - Medical image analysis, 2019 - Elsevier
Deep fully convolutional neural network (FCN) based architectures have shown great
potential in medical image segmentation. However, such architectures usually have millions …
potential in medical image segmentation. However, such architectures usually have millions …