SPAM recognition, global registration

The SPAM sulci recognition is a newer alternative to the older recognition process. Several variants of it are available under the SPAM recognition process. This one allows two methods: "Talairach" (more basic, much faster), and "global registration".

Description

SPAM recognition is based on a probabilistic model which provides for any 3D position the probability of presence of every sulcus. Several variants and additions may also be taken into account (conjoint registration, additional prior contstraints, mix with the older neural network-based recognition, etc.) in a bayesian probabilistic framework.

The current version is the first step, and allows two modes:

"Talairach" mode is the most "basic" implementation. It performs SPAM estimation of the probabilities in the Talairach space, directly. It is very fast.

"Global registration" optimizes both the sulci labelings and an affine registration between the cortical data of the current subject and the SPAM maps. Conjoint registration helps the labeling process, and the output transformation can be actually used as registration information.

Other methods (local registration, Markovian) can be accessed via the general process SPAM recognition.

For a more precise description of the method, see:

• M. Perrot, D. Rivière, and J.-F. Mangin. Cortical sulci recognition and spatial normalization. Medical Image Analysis, 15(4):529-550, 2011.
• M. Perrot, D. Rivière, A. Tucholka, and J.-F. Mangin. Joint Bayesian Cortical Sulci Recognition and Spatial Normalization. In Proc. 21th IPMI, LNCS-5636, Williamsburg, VA, pages 176-187, July 2009..

Warning: additional model data are required:

The process uses learned SPAM models, which are a little too big to be distributed with the main BrainVISA package. They are distributed as separate packages which should be installed on the BrainVISA distribution. They can be very easily installed using the process SPAM install models.

Parameters

data_graph: Cortical folds graph ( input )

Data to be labelled

output_graph: Labelled Cortical folds graph ( output )

Output graph (labelled)

model_type: Choice ( input )

model: Sulci Segments Model ( input )

posterior_probabilities: Sulci Labels Segmentwise Posterior Probabilities ( output )

labels_translation_map: Label translation ( input )

labels_priors: Sulci Labels Priors ( input )

output_transformation: Sulci Talairach to Global SPAM transformation ( optional, output )

initial_transformation: Transformation matrix ( optional, input )

output_t1_to_global_transformation: Raw T1 to Global SPAM transformation ( optional, output )

Technical information

Toolbox : Morphologist

User level : 1

Identifier : spam_recognitionglobal

File name : brainvisa/toolboxes/morphologist/processes/Sulci/Recognition/spam_recognitionglobal.py

Supported file formats :

data_graph :

Graph and data

output_graph :

Graph and data

model :

Text Data Table

posterior_probabilities :

CSV file

labels_translation_map :

Label Translation, DEF Label Translation

labels_priors :

Text Data Table

output_transformation :

Transformation matrix

initial_transformation :

Transformation matrix

output_t1_to_global_transformation :

Transformation matrix