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 2011 - University of Szeged 5/4/2019

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

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

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 2011 - University of Szeged 5/4/2019

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;211:101-110 ©1999 by Radiological Society of North America

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 2011 - University of Szeged 5/4/2019

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 2011 - University of Szeged 5/4/2019

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 2011 - University of Szeged 5/4/2019

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 2011 - University of Szeged 5/4/2019

Two-compartment PK model A=2.12, kep=2.25 Hoffmann-Brix model A=1.27, kep=0.47 Hungary Slicer Training 2011 - University of Szeged 5/4/2019

Correlation coefficient mapping Spearman’s rank order correlation coefficient mapping reference curve: mean malignant (washout) curve Hungary Slicer Training 2011 - University of Szeged 5/4/2019

Statistical analysis stepwise logistic regression compare ANN output with other parameters 250 benign and 250 malignant pixels Hungary Slicer Training 2011 - University of Szeged 5/4/2019

Image analysis software Developed in Matlab R13 Image post-processing Windowing-zooming ROI function Input: 3D Analyze files 5/4/2019 Hungary Slicer Training 2011 - University of Szeged

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Results: diagnostic performance Human reader: sensitivity=100%, specificity=66% ANN: sensitivity=71%, specificity=100% Hungary Slicer Training 2011 - University of Szeged 5/4/2019

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 2011 - University of Szeged 5/4/2019

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 2011 - University of Szeged 5/4/2019