Kim HS

Introduction considering that the amount of MRI data to analyze in present-day clinical trials is often on the order of hundreds or thousands of scans, even minor manual involvement for each scan is an arduous task. The development of fully automatic analysis techniques is desirable to further reduce both the operator time requirements and the measurement variability. At the McConnell Brain Imaging Centre (BIC), we have developed INSECT (Intensity Normalized Stereotaxic Environment for Classification of Tissues), a system aimed at the fully automatic quantification of tissue types in medical image data. Crucial elements of such validation studies are the assessment of accuracy and reproducibility. In the case of INSECT, results obtained on the same data are perfectly reproducible, which is a considerable advantage over manual lesion delineation.

Methods Fig. 1 shows the general architecture of INSECT. The central module of this system is the registration of the data with, and resampling into, a standardized, stereotaxic brain space based on the Talairach atlas. for this application, INSECT employs a back-propagation artificial neural network (ANN), which has been trained once to separate MS lesion from background (non- lesion). The classifier uses six input features, being the TI-, T2-, and PD weighted MRI volumes, as well as three (white matter, gray matter, CSF) SPAMs (Statistical Probability of Anatomy Maps), derived from normal human neuroanatomy.

We have selected a supervised multispectral pattern recognition approach because, as opposed to unsupervised methods, they permit the interactive fine- tuning of the classifiers by adding and removing points in the training set, thus leaving the user in total control of the classification process. ANN’s are capable of generating good segmentation results with very few training points, thus reducing the amount of user interaction.

Definition of Artificial neural networks(ANN) One type of network sees the nodes as 'artificial neurons'. These are called artificial neural networks (ANNs). An artificial neuron is a computational model inspired in the natural neurons. Natural neurons receive signals through synapses located on the dendrites or membrane of the neuron. When the signals received are strong enough (surpass a certain threshold), the neuron is activated and emits a signal through the axon. This signal might be sent to another synapse, and might activate other neurons.

These basically consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron. Another function (which may be the identity) computes the output of the artificial neuron (sometimes in dependance of a certain threshold).

Back-propagation The backpropagation algorithm is used in layered ‘feed-forward’ ANNs. This means that the artificial neurons are organized in layers, and send their signals "forward", and then the errors are propagated backwards. The backpropagation algorithm uses supervised learning, which means that we provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error is calculated. The idea of the backpropagation algorithm is to reduce this error, until the ANN learns the training data. The training begins with random weights, and the goal is to adjust them so that the error will be minimal.

Training In the training phase, the correct class for each record is known (this is termed supervised training), and the output nodes can therefore be assigned "correct" values -- "1" for the node corresponding to the correct class, and "0" for the others. It is thus possible to compare the network's calculated values for the output nodes to these "correct" values, and calculate an error term for each node. These error terms are then used to adjust the weights in the hidden layers so that, hopefully, the next time around the output values will be closer to the "correct" values.

Training Updating weight values – To the minimum of cost function ( Gradient Descent Method) – Cost function of the error  c : # of node at output layer  z k : Value of node k at output layer  y k : Supervisor learning Feed forward Error Backpropagation

Finding 1)Find the weights related to output layer 2)Find the weights related to hidden layer ① ②

Finding Impossible to find partial differentiation directly. Chain rule Activation function

Finding Same as finding

Finding

Addition Determine Activation function Add momentum term at iteration Stopping criteria – Difference between output value and Solution. Feed forward Error Backpropagation

The topology of the ANN’'S has been kept constant in the study presented here: three input nodes, one hidden layer with 10 nodes, and one output layer with five output nodes. Each input node corresponds to one of the imaging modalities (i.e., the TI-, proton density- (PD-), or Tz-weighted image)and each output node corresponds to one of the possible classes: background, white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and WML. ………….. Hidden Layer(10) Output Layer(5) Input Layer(3) background WM GM CSF WML T1 intensity T2 intensity PD intensity Tissue Classification using ANN