Integration of Neural Network Algorithms

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Objective:

  • Integrate artificial neural network segmentation into the NA-MIC kit for segmentation of upper extremity regions of interest.

Progress:

  • Base algorithm has been developed using the annie neural network library
  • Segmentation algorithm has been applied to segmentation of the phalanx bones on the index finger
    • Paper has been published on this initial work


To Do:

  • Initial evaluation of this technique has been performed for phalanx bones.
  • Need to rework ITK neural network libraries to allow the size of the network to be dynamic configured at run time instead of compile time
    • There are several issues that neeed to be resolved regarding the Neural network I/O in ITK
    • Consider the inclusion of the old ANN code that existed in the BRAINS software

Key Investigators:

  • Iowa: Nicole DeVries, Nicole Grosland, Vincent Magnotta


Results

ANN Relative Overlap with Manual Rater
Subject Proximal Phalanx Middle Phalanx Distal Phalanx
1 0.91 0.79 0.79
2 0.91 0.88 0.84
3 0.85 0.83 0.78
4 0.86 0.81 0.68
5 0.84 0.78 0.72
Average 0.87 0.82 0.76



Average Distance Between ANN Derived Surface and Physical Laser Scanned Surface
Subject Proximal Phalanx (mm) Middle Phalanx (mm) Distal Phalanx (mm)
1 0.23 0.12 0.17
2 0.18 0.16 0.16
3 0.35 0.27 0.97
4 0.26 0.17 0.20
Bone Average 0.26 0.18 0.38


Links:

Figures:

Comparison of the manual and ANN segmentations of the phalanx bones. Manual segmentations are shown in red and automated in blue.