Documentation/4.1/Modules/TrainModel

Introduction and Acknowledgements

Extension: LesionSegmentation Acknowledgments: This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Author: Mark Scully () Contact: Mark Scully, <email>mark@biomedicalmining.com</email>

National Alliance for Medical Image Computing (NA-MIC)

Module Description

This module is used to train new segmentation models for white matter lesion segmentation. In order to use this tool your data must include a T1, T2, FLAIR, brain mask, and expert lesion segmentation for each subject. All data must be preprocessed including intra-subject co-registration, AC-PC alignment, bias correction, consistent spacing between sequences, and brain mask creation.

Use Cases

1. Training a new model.

In order to train a new model you must first have preprocessed data on a number of subjects. The data required includes T1, T2, FLAIR, brain masks, and lesion masks. All data must be preprocessed including intra-subject co-registration, AC-PC alignment, bias correction, consistent spacing between sequences, and brain mask creation. Subjects do not need to be registered to each other. A model can be created on a single subject, but greater than 6 is recommended and between 10 and 15 is best. The more subjets included in the model the slower model creation will be and the slower segmentation using that model will be. However, models using more subjects will almost always be more accurate.

Navigate to Modules->Segmentation->LesionSegmentation->TrainModel. The TrainModel panel looks like: (Image of TrainModel panel to go here.)

The required inputs are

Tutorials

Coming soon!

Panels and their use

A list panels in the interface, their features, what they mean, and how to use them.

Input Options: Input options for lesion training. Input Lesion Volumes [----inputLesionVolumes] : Required: A comma separated list of Lesion images Input T1 Volumes [----inputT1Volumes] : Required: A comma separated list of T1 images Input T2 Volumes [----inputT2Volumes] : Required: A comma separated list of T2 images Input FLAIR Volumes [----inputFLAIRVolumes] : Required: A comma separated list of FLAIR images Input Mask Volumes [----inputMaskVolumes] : Required: A comma separated list of Brain Mask images Highest Quality Images Index [----inputIndexOfBestImages] : The index in the list of images that represents the best T1, T2, and FLAIR images. These images will be used as the standard that the other images are intensity standardized to. It defaults to the first image in the list. (This number is 1-indexed) Default value: 1 Advanced Options: Advanced input parameters Percent NonLesion [----inputPercentNonLesion] : What percent of the nonlesion voxels should be used for training. Higher numbers results in larger model files and potentially slower runtimes. Default value: 5 Output Options: Output Options Output Model Filename [----outputModel] : Required: Filename to save the generated model to.

User Interface

Similar Modules

• LesionSegmentation->PredictLesions

References

• Scully M, Anderson B, Lane T, Gasparovic C, Magnotta V, Sibbitt W, Roldan C, Kikinis R and Bockholt HJ (2010) An automated method for segmenting white matter lesions through multi-level morphometric feature classification with application to lupus. Front. Hum. Neurosci. doi:10.3389/fnhum.2010.00027

http://frontiersin.org/neuroscience/humanneuroscience/paper/10.3389/fnhum.2010.00027/