Fully Automatic Lesion Localization and Characterization: Application to Brain Tumors Using Multiparametric Quantitative MRI Data

• Published in: IEEE transactions on medical imaging Vol. 37; no. 7; pp. 1678 - 1689
• Main Authors: Arnaud, Alexis, Forbes, Florence, Coquery, Nicolas, Collomb, Nora, Lemasson, Benjamin, Barbier, Emmanuel L.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; animal models and imaging; Animals; anomaly detection; Applications; automatic characterization; automatic segmentation; Automation; Brain; Brain cancer; Brain Neoplasms - diagnostic imaging; brain tumor; Brain tumors; Cancer; computer-aided detection and diagnosis; Data processing; Databases, Factual; Feature extraction; fingerprint model; Heterogeneity; Image Interpretation, Computer-Assisted - methods; Image processing; Image segmentation; Lesions; Life Sciences; Localization; Magnetic resonance imaging; magnetic resonance imaging (MRI); Magnetic Resonance Imaging - methods; Mathematical models; Medical diagnosis; Methodology; mixture model; Normal distribution; Parameters; Pathology; Perfusion imaging; Physiology; probabilistic and statistical methods; quantitative multiparametric MRI; radiomics; Rats; Shape; Signatures; Spatial discrimination; Statistical analysis; Statistics; Tissues; Tumors; Animal models and imaging; Fingerprint model; Magnetic resonance imaging (MRI); Mixture model; Computer-aided detection and diagnosis; Anomaly detection; Radiomics; Brain tumor; Probabilistic and statistical methods; Automatic characterization; Automatic segmentation; Perfusion imaging; Quantitative multiparametric MRI
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2018.2794918

When analyzing brain tumors, two tasks are intrinsically linked, spatial localization, and physiological characterization of the lesioned tissues. Automated data-driven solutions exist, based on image segmentation techniques or physiological parameters analysis, but for each task separately, the other being performedmanually or with user tuning operations. In this paper, the availability of quantitative magnetic resonance (MR) parameters is combined with advancedmultivariate statistical tools to design a fully automated method that jointly performs both localization and characterization. Non trivial interactions between relevant physiologicalparameters are capturedthanks to recent generalized Student distributions that provide a larger variety of distributional shapes compared to the more standard Gaussian distributions. Probabilisticmixtures of the former distributions are then consideredto account for the different tissue types and potential heterogeneity of lesions. Discriminative multivariate features are extracted from this mixture modeling and turned into individual lesion signatures. The signatures are subsequently pooled together to build a statistical fingerprintmodel of the different lesion types that captures lesion characteristics while accounting for inter-subject variability. The potential of this generic procedure is demonstrated on a data set of 53 rats, with 36 rats bearing 4 different brain tumors, for which 5 quantitative MR parameters were acquired.