Add to ORKGAnonymised human CT data sets were automatically segmented into bone, fat, and other soft tissue based on Hounsfield units. In some cases semi-automatic segmentation of organs such as: kidneys, liver, pancreas and spleen has been carried out
Purpose: We describe a public dataset with MR and CT images of patients performed in the same positi...
In the recent years a great deal of research work has been devoted to the development of semi-automa...
In humans CT imaging is a validated method for the study of adipose tissue distribution and for quan...
Segmentation of thigh CT images into regions of fat, muscle, and bone based on user-defined Hounsfie...
Automatic segmentation of human organs allows more accurate calculation of organ doses in radiationt...
We have been developing a computer-aided diagnosis (CAD) scheme for automatically recognizing human ...
n the recent years a great deal of research work has been devoted to the development of semi-automat...
In the field of medical image processing, one of the current interests is the automatic segmentatio...
In 1204 CT images we segmented 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels...
Analysis of medical images is resource demanding and time-consuming, and automatic procedures are ne...
Purpose To develop and validate a computer tool for automatic and simultaneous segmentation of five ...
The study addresses the challenging problem of automatic segmentation of the human anatomy needed fo...
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI are providing pr...
Objective Computed tomography images are becoming an invaluable mean for abdominal organ investigati...
Purpose: We describe a public dataset with MR and CT images of patients performed in the same positi...
Purpose: We describe a public dataset with MR and CT images of patients performed in the same positi...
In the recent years a great deal of research work has been devoted to the development of semi-automa...
In humans CT imaging is a validated method for the study of adipose tissue distribution and for quan...
Segmentation of thigh CT images into regions of fat, muscle, and bone based on user-defined Hounsfie...
Automatic segmentation of human organs allows more accurate calculation of organ doses in radiationt...
We have been developing a computer-aided diagnosis (CAD) scheme for automatically recognizing human ...
n the recent years a great deal of research work has been devoted to the development of semi-automat...
In the field of medical image processing, one of the current interests is the automatic segmentatio...
In 1204 CT images we segmented 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels...
Analysis of medical images is resource demanding and time-consuming, and automatic procedures are ne...
Purpose To develop and validate a computer tool for automatic and simultaneous segmentation of five ...
The study addresses the challenging problem of automatic segmentation of the human anatomy needed fo...
Deep Learning approaches for automatic segmentation of organs from CT scans and MRI are providing pr...
Objective Computed tomography images are becoming an invaluable mean for abdominal organ investigati...
Purpose: We describe a public dataset with MR and CT images of patients performed in the same positi...
Purpose: We describe a public dataset with MR and CT images of patients performed in the same positi...
In the recent years a great deal of research work has been devoted to the development of semi-automa...
In humans CT imaging is a validated method for the study of adipose tissue distribution and for quan...