Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features
Posted on 2019-04-27 - 05:00
Abstract Background Multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) shows clinical potential for detection and classification of breast lesions. Yet, the contribution of features for computer-aided segmentation and diagnosis (CAD) need to be better understood. We proposed a data-driven machine learning approach for a CAD system combining dynamic contrast-enhanced (DCE)-MRI, diffusion-weighted imaging (DWI), and 18F-fluorodeoxyglucose (18F-FDG)-PET. Methods The CAD incorporated a random forest (RF) classifier combined with mpPET/MRI intensity-based features for lesion segmentation and shape features, kinetic and spatio-temporal texture features, for lesion classification. The CAD pipeline detected and segmented suspicious regions and classified lesions as benign or malignant. The inherent feature selection method of RF and alternatively the minimum-redundancy-maximum-relevance feature ranking method were used. Results In 34 patients, we report a detection rate of 10/12 (83.3%) and 22/22 (100%) for benign and malignant lesions, respectively, a Dice similarity coefficient of 0.665 for segmentation, and a classification performance with an area under the curve at receiver operating characteristics analysis of 0.978, a sensitivity of 0.946, and a specificity of 0.936. Segmentation but not classification performance of DCE-MRI improved with information from DWI and FDG-PET. Feature ranking revealed that kinetic and spatio-temporal texture features had the highest contribution for lesion classification. 18F-FDG-PET and morphologic features were less predictive. Conclusion Our CAD enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. It may aid as a novel computational tool for exploring different modalities/features and their contributions for the detection and classification of breast lesions.
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Vogl, Wolf-Dieter; Pinker, Katja; Helbich, Thomas; Bickel, Hubert; Grabner, GĂźnther; Bogner, Wolfgang; et al. (2019). Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.4486448.v1
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AUTHORS (10)
WV
Wolf-Dieter Vogl
KP
Katja Pinker
TH
Thomas Helbich
HB
Hubert Bickel
GG
GĂźnther Grabner
WB
Wolfgang Bogner
SG
Stephan Gruber
ZB
Zsuzsanna Bago-Horvath
PD
Peter Dubsky
GL
Georg Langs