Automated detection of white matter hyperintensities of all sizes in cerebral small vessel disease. Issue 12 (2nd November 2016)

• Record Type: Journal Article
• Authors: Ghafoorian, Mohsen; Karssemeijer, Nico; van Uden, Inge W. M.; de Leeuw, Frank‐Erik; Heskes, Tom; Marchiori, Elena; Platel, Bram
• Is Part Of: Medical physics. Volume 43:Issue 12(2016)
• Page Start: 6246
• Page End: 6258
• Publication Date: 2016-11-02
• Subjects: biological tissues -- biomedical MRI -- brain -- diseases -- image segmentation -- learning (artificial intelligence) -- medical disorders -- medical image processing -- neurophysiology; Magnetic resonance imaging -- Diseases -- Segmentation; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- In which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Inference methods or devices; automated detection -- white matter hyperintensity -- computer aided detection -- Small vessel disease -- adaboost; Brain -- Tissues -- Dementia -- Medical magnetic resonance imaging -- Testing procedures -- Machine learning -- Learning -- Systems analysis; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4966029
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: White matter hyperintensities (WMH) are seen on FLAIR‐MRI in several neurological disorders, including multiple sclerosis, dementia, Parkinsonism, stroke and cerebral small vessel disease (SVD). WMHs are often used as biomarkers for prognosis or disease progression in these diseases, and additionally longitudinal quantification of WMHs is used to evaluate therapeutic strategies. Human readers show considerable disagreement and inconsistency on detection of small lesions. A multitude of automated detection algorithms for WMHs exists, but since most of the current automated approaches are tuned to optimize segmentation performance according to Jaccard or Dice scores, smaller WMHs often go undetected in these approaches. In this paper, the authors propose a method to accurately detect all WMHs, large as well as small. Methods: A two‐stage learning approach was used to discriminate WMHs from normal brain tissue. Since small and larger WMHs have quite a different appearance, the authors have trained two probabilistic classifiers: one for the small WMHs (⩽3 mm effective diameter) and one for the larger WMHs (>3 mm in‐plane effective diameter). For each size‐specific classifier, an Adaboost is trained for five iterations, with random forests as the basic classifier. The feature sets consist of 22 features including intensities, location information, blob detectors, and second order derivatives. The outcomes of the two first‐stage classifiers were combined into a single WMH likelihood by a second‐stage classifier. Their method was trained and evaluated on a dataset with MRI scans of 362 SVD patients (312 subjects for training and validation annotated by one and 50 for testing annotated by two trained raters). To analyze performance on the separate test set, the authors performed a free‐response receiving operating characteristic (FROC) analysis, instead of using segmentation based methods that tend to ignore the contribution of small WMHs. Results: Experimental results based on FROC analysis demonstrated a close performance of the proposed computer aided detection (CAD) system to human readers. While an independent reader had 0.78 sensitivity with 28 false positives per volume on average, their proposed CAD system reaches a sensitivity of 0.73 with the same number of false positives. Conclusions: The authors have developed a CAD system with all its ingredients being optimized for a better detection of WMHs of all size, which shows performance close to an independent reader.