Deep learning in medical imaging and radiation therapy
The goals of this review paper on deep learning (DL) in medical imaging and radiation
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
therapy are to (a) summarize what has been achieved to date;(b) identify common and …
A survey on deep learning in medical image analysis
Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …
methodology of choice for analyzing medical images. This paper reviews the major deep …
VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images
Segmentation of key brain tissues from 3D medical images is of great significance for brain
disease diagnosis, progression assessment and monitoring of neurologic conditions. While …
disease diagnosis, progression assessment and monitoring of neurologic conditions. While …
Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation
Accurate and automatic organ segmentation from 3D radiological scans is an important yet
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
challenging problem for medical image analysis. Specifically, as a small, soft, and flexible …
Prior-aware neural network for partially-supervised multi-organ segmentation
Accurate multi-organ abdominal CT segmentation is essential to many clinical applications
such as computer-aided intervention. As data annotation requires massive human labor …
such as computer-aided intervention. As data annotation requires massive human labor …
Deepvesselnet: Vessel segmentation, centerline prediction, and bifurcation detection in 3-d angiographic volumes
We present DeepVesselNet, an architecture tailored to the challenges faced when extracting
vessel trees and networks and corresponding features in 3-D angiographic volumes using …
vessel trees and networks and corresponding features in 3-D angiographic volumes using …
High-resolution encoder–decoder networks for low-contrast medical image segmentation
Automatic image segmentation is an essential step for many medical image analysis
applications, include computer-aided radiation therapy, disease diagnosis, and treatment …
applications, include computer-aided radiation therapy, disease diagnosis, and treatment …
Progressive and multi-path holistically nested neural networks for pathological lung segmentation from CT images
Pathological lung segmentation (PLS) is an important, yet challenging, medical image
application due to the wide variability of pathological lung appearance and shape. Because …
application due to the wide variability of pathological lung appearance and shape. Because …
Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks
Accurate automatic segmentation of the prostate in magnetic resonance images (MRI) is a
challenging task due to the high variability of prostate anatomic structure. Artifacts such as …
challenging task due to the high variability of prostate anatomic structure. Artifacts such as …
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 …