Class JDialogProstateXReRunWholeProstate

java.lang.Object
java.awt.Component
java.awt.Container
java.awt.Window
java.awt.Dialog
javax.swing.JDialog
gov.nih.mipav.view.dialogs.JDialogBase
gov.nih.mipav.view.renderer.WildMagic.ProstateFramework.JDialogProstateXReRunWholeProstate
All Implemented Interfaces:
AlgorithmInterface, DialogDefaultsInterface, ActionListener, FocusListener, ItemListener, WindowListener, ImageObserver, MenuContainer, Serializable, EventListener, Accessible, RootPaneContainer, WindowConstants

public class JDialogProstateXReRunWholeProstate extends JDialogBase implements AlgorithmInterface
For NIH prostate data, we apply wp and cg segmentation using HED deep learning model. Data: given NIH prostate data, axial images with corresponing VOIs for Cg and Wp. Steps: 1. read image file and VOIs 2. From VOIs, generate the binary mask images. 3. Transform into isotropic image (upsampling) with resolution: 0.35m x 0.35m x 0.35m. 4. Conversion: converts axial image to axial an coronal using JDialogReoriented. 5. Generate the CED images from MR images in three orientation: axial, sagittal, coronal. 6. Each orientation, save 3D MR images, CED images with corresponding binary mask images. This is the 2D-volumetric segmentation approach.
Author:
Ruida Cheng
See Also: