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Neural Network Segmentation and Validation Nicole M. Grosland Vincent A. Magnotta.

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Presentation on theme: "Neural Network Segmentation and Validation Nicole M. Grosland Vincent A. Magnotta."— Presentation transcript:

1 Neural Network Segmentation and Validation Nicole M. Grosland Vincent A. Magnotta

2 Objective To develop tools to automate bony structure mesh definitions suitable for patient-specific finite element contact analyses. –Further, automate the identification of the structures of the upper extremity (including hand/fingers, wrist, elbow and shoulder) using a neural network.

3 Specific Aims Aim 1: Integrate and enhance a set of novel and robust hexahedral mesh generation algorithms into the NA-MIC toolkit. Aim 2: Further automate these modeling capabilities by developing tools for automated image region identification via neural networks. Aim 3: Validate geometry of models using cadaveric specimens and three-dimensional surface scans

4 Imaging Protocol 15 cadaveric specimens were acquired and imaged CT images, Siemens Sensation 64 CT scanner (matrix = 512x512, KVP = 120). –0.34-mm in-plane resolution –0.4 mm slice thickness MR images: Siemens 3T Trio scanner –PD weighted images – 2D FSE TE=12ms, TR=7060ms Resolution=0.5x0.5mm Slice Thickness = 1.0mm Matrix=512x512 –T1 weighted images – 3D MP-RAGE TE=3.35ms, TR=2530ms, TI=1100ms Resolution=0.6x0.6x0.5mm Matrix=384x384x96 Post-processing via BRAINS2 –Spatially normalized –Resampled to 0.2-mm3 voxels

5 Manual Segmentation Two trained technicians (Tracer1 and Tracer2) manually traced twenty-one phalanx bones (index) –the distal, middle, and proximal bones Relative overlap: Records maintained of tracing times

6 Neural Network Data Spherical Coordinates Gradient Values Area Iris Values Probability Map Values Input Vector: {P S1, P S2, S α, S β, S γ, G -4, … G 4, A 1, … A 12 } Mask Values Output Vector: {M S1, M S2 }

7 Neural Network Configuration Output Layer test Calculated Error Backpropagation Input Layer Hidden Layer

8 Neural Network Training 10 subjects used to train the neural network –Subjects all registered to atlas dataset –Manual segmentations used to define probability information –200,000 input vectors x 250 iterations 5 subjects used to evaluate validity and reliability of network

9 3D Laser Scanner 3D Laser scanners have been used for rapid prototyping and to non- destructively image ancient artifacts Roland LPX-250 Scanner Obtained –Planar and rotary scanning modes –0.008 inch resolution in planar mode –Objects up to 10 inches wide and 16 inches tall can be scanned –Reverse modeling software tools

10 LPX-250 Laser Scanner

11 Finger Dissection Phalanx and metacarpal bones removed –Care taken to avoid tool marks on the bones De-fleshing process outlined by Donahue et al (2002) was utilized –Bones allowed to soak in a 5.25% sodium hypochlorite (bleach) solution for 6 hours Degreased via a soapy water solution Thin layer of white primer was used to coat the bony surfaces

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13 SC RMD LMD RCA L

14 Registration of Surfaces Surface scans origin shifted to center of mass and reoriented to have the same orientation as the CT data Surfaces registered using a rigid iterative closest point algorithm Compute Euclidean distance between the surfaces

15 a b c d Specimen CA L Manual (red) and ANN (blue) ROI definitions

16 Manual Segmentation Relative overlap (Tracer1 and Tracer2) –0.89 for the three bones. –Individual bones Proximal – 0.91 Middle – 0.90 Distal bones – 0.87 The average time required to manually segment the bones of the index finger was 50.9 minutes, ranging from 39 to 63 minutes.

17 Subject IDProximal Overlap Middle Overlap Distal Overlap Index Finger Overlap CA R CA L MD R MD L SC R All Subjects Relative Overlap of Manual and Neural Network Segmentation ANN Results Compared to Manual Rater

18 ANN output & 3D physical surface scans Example Distance Maps

19 ANN Validation Subject IDProximal Phalanx (mm) Middle Phalanx (mm) Distal Phalanx (mm) Finger Average (mm) CA L MD R MD L SC R Bone Average ANN output & 3D physical surface scans

20 Conclusion Neural networks provide a promising automated segmentation tool for identifying bony regions of interest Output was compared to both manual raters and 3D surface scanning –Error was less than the size of 1 voxel Use of 3D surface scanning provides a means to have a true gold standard for evaluation of automated segmentation algorithms

21 Acknowledgements Grant funding –R21 (EB001501) –R01 (EB005973) Stephanie Powell, Nicole Kallemeyn, Nicole DeVries, Esther Gassman

22 Validation Aim 3: Model Validation: Cadaveric specimens will be used (i) to generate three-dimensional surface scans with which surfaces defined both manually and via the automated neural network will be compared and (ii) to directly validate the computational models developed via the automated meshing algorithms.

23 Validation True gold-standard often very difficult to achieve –Brain imaging often have to live with manual raters –Established guidelines based on anatomical experts Are there better gold-standards for other regions of the body?

24 Orthopaedic Imaging Ideas developed out of goal to automate the definition of bony regions of interest. How can we validate these automated tools? Orthopaedic applications: Would it be possible to dissect cadaveric specimens? –Use bony specimen as the gold-standard

25 Surface Comparison Average Distance (mm) Maximum Distance (mm) Proximal Middle Distal Physical surface scan (white) Manually segmented surface (blue) ProximalMiddleDistal

26 Manual surface definitions with various degrees of smoothing (a)Unsmoothed, (b)Image-based smoothing, & (c)Laplacian surface-based smoothing. abcabc

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29 Average Euclidean distance and standard deviation between the manually traced unsmoothed surfaces and the physical surface scans. Finger IDProximal Phalanx (mm) Middle phalanx (mm) Distal phalanx (mm) Finger Average (mm) CA L0.19 (0.43)0.12 (0.16)0.15 (0.19)0.15 MD R0.04 (0.08)0.06 (0.09)0.05 (0.07)0.05 MD R0.05 (0.07) 0.06 (0.08)0.05 MD L0.21 (0.32)0.24 (0.35)0.21 (0.29)0.22 SC R0.20 (0.35)0.10 (0.15)0.10 (0.18)0.13 Bone Average

30 Average Euclidean distance and standard deviation between the surfaces generated via image-based smoothing and the physical surface scans. Finger IDProximal Phalanx (mm) Middle phalanx (mm) Distal phalanx (mm) Finger Average (mm) CA L0.32 (0.31)0.27 (0.19)0.32 (0.25)0.31 MD R0.16 (0.15)0.24 (0.19)0.28 (0.23)0.22 MD R0.14 (0.10)0.18 (0.14)0.19 (0.14)0.17 MD L0.38 (0.30)0.41 (0.35)0.42 (0.37)0.40 SC R0.36 (0.31)0.24 (0.20)0.27 (0.26)0.21 Bone Average

31 Average Euclidean distance and standard deviations between the surfaces generated via Laplacian surface- based smoothing and the physical surfaces. Finger IDProximal Phalanx (mm) Middle phalanx (mm) Distal phalanx (mm) Finger Average (mm) CA L0.18 (0.48)0.10 (0.15)0.21 (0.39)0.16 MD R0.09 (0.15)0.17 (0.22)0.25 (0.37)0.17 MD R0.03 (0.06)0.08 (0.13)0.06 (0.09)0.06 MD L0.22 (0.37)0.32 (0.54)0.37 (0.67)0.30 SC R0.26 (0.45)0.11 (0.17)0.21 (0.41)0.19 Bone Average Average Euclidean distance and standard deviations between the surfaces generated via Laplacian surface-based smoothing and the physical surfaces.

32 a b c

33 Neural Networks A computing paradigm that is designed to modeled how the brain processes data The network consists of several interconnected neurons that process that the input information through and activation function to form an output What information can be used to segment regions of interest from images


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