[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 …
Current applications and future directions of deep learning in musculoskeletal radiology
P Chea, JC Mandell - Skeletal radiology, 2020 - Springer
Deep learning with convolutional neural networks (CNN) is a rapidly advancing subset of
artificial intelligence that is ideally suited to solving image-based problems. There are an …
artificial intelligence that is ideally suited to solving image-based problems. There are an …
Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and …
B Norman, V Pedoia, S Majumdar - Radiology, 2018 - pubs.rsna.org
Purpose To analyze how automatic segmentation translates in accuracy and precision to
morphology and relaxometry compared with manual segmentation and increases the speed …
morphology and relaxometry compared with manual segmentation and increases the speed …
Automated segmentation of knee bone and cartilage combining statistical shape knowledge and convolutional neural networks: Data from the Osteoarthritis Initiative
We present a method for the automated segmentation of knee bones and cartilage from
magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape …
magnetic resonance imaging (MRI) that combines a priori knowledge of anatomical shape …
Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging
Purpose To describe and evaluate a new fully automated musculoskeletal tissue
segmentation method using deep convolutional neural network (CNN) and three …
segmentation method using deep convolutional neural network (CNN) and three …
Deep convolutional neural network for segmentation of knee joint anatomy
Purpose To describe and evaluate a new segmentation method using deep convolutional
neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex …
neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex …
SDMT: spatial dependence multi-task transformer network for 3D knee MRI segmentation and landmark localization
Knee segmentation and landmark localization from 3D MRI are two significant tasks for
diagnosis and treatment of knee diseases. With the development of deep learning …
diagnosis and treatment of knee diseases. With the development of deep learning …
The kits21 challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase ct
This paper presents the challenge report for the 2021 Kidney and Kidney Tumor
Segmentation Challenge (KiTS21) held in conjunction with the 2021 international …
Segmentation Challenge (KiTS21) held in conjunction with the 2021 international …
Challenges in diffusion MRI tractography–Lessons learned from international benchmark competitions
Diffusion MRI (dMRI) fiber tractography has become a pillar of the neuroimaging community
due to its ability to noninvasively map the structural connectivity of the brain. Despite …
due to its ability to noninvasively map the structural connectivity of the brain. Despite …
Skm-tea: A dataset for accelerated mri reconstruction with dense image labels for quantitative clinical evaluation
Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However,
long image acquisition times, the need for qualitative expert analysis, and the lack of (and …
long image acquisition times, the need for qualitative expert analysis, and the lack of (and …