1. ANN-based image segmentation and classification for dynamic contrast-enhanced breast MRI
Botond K. Szabó *
Peter Aspelin **
Maria Kristoffersen-Wiberg **
* Department of Radiology, University of Szeged
**Department of Clinical Science, Internvention and Technology,
Karolinska Institutet, Sweden
Hungary Slicer Training University of Szeged
5/4/2019

2. Indications for breast MRI
Breast implants failure
Preoperative staging of lobular ca
Monitoring the effect of chemotherapy
Postoperative follow-up
Detection of occult carcinomas
Screening in high-risk women
Center for Surgical Sciences, Karolinska Institutet
04/05/2019

3. MRI of the breast Non-enhanced MRI: breast implants
Gd-DTPA-enhanced dynamic MRI: detection of breast cancer
Features of contrast enhancement used for image interpretation: - amount - morphology - kinetics
Center for Surgical Sciences, Karolinska Institutet
04/05/2019

4. Dynamic contrast-enhanced MRI of the breast /DCE-MRI/
Dynamic study: 1 pre + 7 post-contrast series
Enhancing areas are suspicious of cancer – assessed on subtraction series
Kinetic curves obtained
Manually with ROI technique
Kinetic information can be displayed using colour coded maps on precontast images (primarily to assist diagnosis)
Hungary Slicer Training University of Szeged
5/4/2019

5. Time-signal intensity curve types
1. continous uptake
2. plateau
3. washout
Schematic drawing of the time-signal intensity curve types. Type I corresponds to a straight (Ia) or curved (Ib) line; enhancement continues over the entire dynamic study. Type II is a plateau curve with a sharp bend after the initial upstroke. Type III is a washout time course ([SIc - SI]/SI).
Kuhl C K et al. Radiology 1999;211:
©1999 by Radiological Society of North America

6. Aims of the study
ANN-based segmentation system for dynamic breast MR images
Comparison with empiric and pharmaco-kinetic parameters
Diagnostic performance
Hungary Slicer Training University of Szeged
5/4/2019

7. Material and Methods (1)
10 histopathologically verified lesions (7 malignant, 3 benign)
MR technique:
1.5 T system
Dynamic study: 1 pre-, 7 postcontrast
T1-weighted 3D-FLASH
TR 8.1 ms, TE 4 ms, FA 200, FOV 320 mm, matrix 96x256, AT 1 min, contrast dose: 0.1 mmol/kg bw.
Hungary Slicer Training University of Szeged
5/4/2019

8. Material and Methods (2)
Affine and non-rigid image registration (VTK-CISG toolkit on Linux)
Tested techniques:
ANN
Subtraction SIsub=SIpost-SIpre+const
Percent enhancement En=(SIn-SIpre)/SIpre*100
Signal enhancement ratio SER=Epeak/E7
Time-to-peak
Correlation coefficient mapping
Two-compartment PK model
Hungary Slicer Training University of Szeged
5/4/2019

9. ANN-based segmentation
Two-layered FFBP ANN (trained on 140 curves)
7 input units: E1-E7
4 hidden units
4 output classes:
M=malignant
B=benign
P=parenchyma
F=fat tissue E1 E2 E3 E4 E5 E6 E7 M B P F
Hungary Slicer Training University of Szeged
5/4/2019

10. Two-compartment PK model
A=2.12, kep=2.25
Hoffmann-Brix model
A=1.27, kep=0.47
Hungary Slicer Training University of Szeged
5/4/2019

11. Correlation coefficient mapping
Spearman’s rank order correlation coefficient mapping
reference curve: mean malignant (washout) curve
Hungary Slicer Training University of Szeged
5/4/2019

12. Statistical analysis stepwise logistic regression
compare ANN output with other parameters
250 benign and 250 malignant pixels
Hungary Slicer Training University of Szeged
5/4/2019

13. Image analysis software
Developed in Matlab R13
Image post-processing
Windowing-zooming
ROI function
Input: 3D Analyze files
5/4/2019
Hungary Slicer Training University of Szeged

14. 5/4/2019
Hungary Slicer Training University of Szeged

15. 5/4/2019
Hungary Slicer Training University of Szeged

16. Results: diagnostic performance
Human reader: sensitivity=100%, specificity=66%
ANN: sensitivity=71%, specificity=100%
Hungary Slicer Training University of Szeged
5/4/2019

17. Results: statistical analysis
Parameters independently related to ANN output:
Correlation coefficient (OR=12.9)
kep (OR=1.8)
Time-to-peak (OR=0.45)
Hungary Slicer Training University of Szeged
5/4/2019

18. Conclusions
ANN method was successfully applied to segmentation and classification of breast DCE-MR images
Mapping correlation coefficient and PK parameters are comparable to ANN
Hungary Slicer Training University of Szeged
5/4/2019