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!

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/