CNN-GCN aggregation enabled boundary regression for biomedical image segmentation
Accurate segmentation of anatomic structure is an essential task for biomedical image
analysis. Recent popular object contours regression based segmentation methods have …
analysis. Recent popular object contours regression based segmentation methods have …
Deep active contour network for medical image segmentation
Image segmentation is vital to medical image analysis and clinical diagnosis. Recently,
convolutional neural networks (CNNs) have achieved tremendous success in this task …
convolutional neural networks (CNNs) have achieved tremendous success in this task …
A survey on shape-constraint deep learning for medical image segmentation
S Bohlender, I Oksuz… - IEEE Reviews in …, 2021 - ieeexplore.ieee.org
Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …
have completely changed the modern landscape of deep-learning based medical image …
A surprisingly effective perimeter-based loss for medical image segmentation
Deep convolutional networks recently made many breakthroughs in medical image
segmentation. Still, some anatomical artefacts may be observed in the segmentation results …
segmentation. Still, some anatomical artefacts may be observed in the segmentation results …
Learning geodesic active contours for embedding object global information in segmentation CNNs
J Ma, J He, X Yang - IEEE Transactions on Medical Imaging, 2020 - ieeexplore.ieee.org
Most existing CNNs-based segmentation methods rely on local appearances learned on the
regular image grid, without consideration of the object global information. This article aims to …
regular image grid, without consideration of the object global information. This article aims to …
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation
B Murugesan, K Sarveswaran… - 2019 41st Annual …, 2019 - ieeexplore.ieee.org
Image segmentation is a primary task in many medical applications. Recently, many deep
networks derived from U-Net has been extensively used in various medical image …
networks derived from U-Net has been extensively used in various medical image …
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 …
Fully convolutional attention network for biomedical image segmentation
In this paper, we embed two types of attention modules in the dilated fully convolutional
network (FCN) to solve biomedical image segmentation tasks efficiently and accurately …
network (FCN) to solve biomedical image segmentation tasks efficiently and accurately …
DRU-Net: an efficient deep convolutional neural network for medical image segmentation
Residual network (ResNet) and densely connected network (DenseNet) have significantly
improved the training efficiency and performance of deep convolutional neural networks …
improved the training efficiency and performance of deep convolutional neural networks …
Robust boundary segmentation in medical images using a consecutive deep encoder-decoder network
Image segmentation is typically used to locate objects and boundaries. It is essential in
many clinical applications, such as the pathological diagnosis of hepatic diseases, surgical …
many clinical applications, such as the pathological diagnosis of hepatic diseases, surgical …